Multi-agent collaboration mechanisms: a survey of LLMs

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dc.contributor.authorTran, Khanh-Tungen
dc.contributor.authorDao, Dungen
dc.contributor.authorNguyen, Minh-Duongen
dc.contributor.authorPham, Quoc-Vieten
dc.contributor.authorO’Sullivan, Barryen
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-04-23T14:24:43Z
dc.date.available2025-04-23T14:24:43Z
dc.date.issued2025en
dc.description.abstractWith 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.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTran, K.-T., Dao, D., Nguyen, M.-D., Pham, Q.-V., O’Sullivan, B. and Nguyen, H.D. (2025) ‘Multi-agent collaboration mechanisms: a survey of LLMs’. arXiv. https://doi.org/10.48550/ARXIV.2501.06322en
dc.identifier.doihttps://doi.org/10.48550/ARXIV.2501.06322en
dc.identifier.endpage35en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17307
dc.language.isoenen
dc.publisherarXiven
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.urihttps://arxiv.org/abs/2501.06322en
dc.rights© 2025, the Authors.en
dc.rights.urihttps://arxiv.org/licenses/nonexclusive-distrib/1.0/license.htmlen
dc.subjectArtificial Intelligenceen
dc.subjectLarge Language Modelen
dc.subjectMulti-agent collaborationen
dc.titleMulti-agent collaboration mechanisms: a survey of LLMsen
dc.typeArticle (peer-reviewed)en
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