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[2501.06322] Multi-Agent Collaboration Mechanisms: A Survey of LLMs

<!DOCTYPE html> <html lang="en"> <head> <title>[2501.06322] Multi-Agent Collaboration Mechanisms: A Survey of LLMs</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2501.06322"/> <meta name="description" content="Abstract page for arXiv paper 2501.06322: Multi-Agent Collaboration Mechanisms: A Survey of LLMs"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Multi-Agent Collaboration Mechanisms: A Survey of LLMs" /> <meta property="og:url" content="https://arxiv.org/abs/2501.06322v1" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Multi-Agent Collaboration Mechanisms: A Survey of LLMs"/> <meta name="twitter:description" content="With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Multi-Agent Collaboration Mechanisms: A Survey of LLMs" /><meta name="citation_author" content="Tran, Khanh-Tung" /><meta name="citation_author" content="Dao, Dung" /><meta name="citation_author" content="Nguyen, Minh-Duong" /><meta name="citation_author" content="Pham, Quoc-Viet" /><meta name="citation_author" content="O&#39;Sullivan, Barry" /><meta name="citation_author" content="Nguyen, Hoang D." /><meta name="citation_date" content="2025/01/10" /><meta name="citation_online_date" content="2025/01/10" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2501.06322" /><meta name="citation_arxiv_id" content="2501.06322" /><meta name="citation_abstract" content="With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a> <!-- start desktop header --> <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" 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<strong>arXiv:2501.06322</strong> (cs) </div> <link rel="stylesheet" type="text/css" href="/static/base/1.0.1/css/abs.css"> <div id="content-inner"> <div id="abs"> <div class="dateline"> [Submitted on 10 Jan 2025]</div> <h1 class="title mathjax"><span class="descriptor">Title:</span>Multi-Agent Collaboration Mechanisms: A Survey of LLMs</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Tran,+K" rel="nofollow">Khanh-Tung Tran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Dao,+D" rel="nofollow">Dung Dao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Nguyen,+M" rel="nofollow">Minh-Duong Nguyen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Pham,+Q" rel="nofollow">Quoc-Viet Pham</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=O&#39;Sullivan,+B" rel="nofollow">Barry O&#39;Sullivan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Nguyen,+H+D" rel="nofollow">Hoang D. Nguyen</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Multi-Agent Collaboration Mechanisms: A Survey of LLMs, by Khanh-Tung Tran and 5 other authors</div> <a class="mobile-submission-download" href="/pdf/2501.06322">View PDF</a> <a class="mobile-submission-download" href="https://arxiv.org/html/2501.06322v1">HTML (experimental)</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence. </blockquote> <!--CONTEXT--> <div class="metatable"> <table summary="Additional metadata"><tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Artificial Intelligence (cs.AI)</span></td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2501.06322">arXiv:2501.06322</a> [cs.AI]</span></td> </tr> <tr> <td class="tablecell label">&nbsp;</td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2501.06322v1">arXiv:2501.06322v1</a> [cs.AI]</span> for this version) </td> </tr> <tr> <td class="tablecell label">&nbsp;</td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2501.06322" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2501.06322</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg> <span class="visually-hidden">Focus to learn more</span> </button> <!-- tooltip description --> <div role="tooltip" id="more-info-desc-1"> <span class="left-corner"></span> arXiv-issued DOI via DataCite</div> </div> </td> </tr></table> </div> </div> </div> <div class="submission-history"> <h2>Submission history</h2> From: Hoang D. Nguyen [<a href="/show-email/8f2b1624/2501.06322" rel="nofollow">view email</a>] <br/> <strong>[v1]</strong> Fri, 10 Jan 2025 19:56:50 UTC (5,002 KB)<br/> </div> </div> <!--end leftcolumn--> <div class="extra-services"> <div class="full-text"> <a name="other"></a> <span class="descriptor">Full-text links:</span> <h2>Access Paper:</h2> <ul> <div id="download-button-info" hidden> View a PDF of the paper titled Multi-Agent Collaboration Mechanisms: A Survey of LLMs, by Khanh-Tung Tran and 5 other authors</div><li><a href="/pdf/2501.06322" aria-describedby="download-button-info" accesskey="f" class="abs-button download-pdf">View PDF</a></li><li><a href="https://arxiv.org/html/2501.06322v1" class="abs-button" id="latexml-download-link">HTML (experimental)</a></li><li><a href="/src/2501.06322" class="abs-button download-eprint">TeX Source</a></li><li><a href="/format/2501.06322" class="abs-button download-format">Other Formats</a></li></ul> <div class="abs-license"><a 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