---
title: "Multi-Agent Collaboration Mechanisms: A Survey of LLMs (2025)"
description: "Large language models still hallucinate, struggle with slow, deliberate reasoning and run into scaling… 166 references, cited by 68 papers here."
authors: ["Khanh-Tung Tran", "Dung Dao", "Minh-Duong Nguyen", "Quoc-Viet Pham", "Barry O'Sullivan", "Hoang D. Nguyen"]
year: 2025
venue: "arXiv:2501.06322"
source: "https://arxiv.org/abs/2501.06322"
doi: "10.48550/arxiv.2501.06322"
arxiv: "2501.06322"
licence: "arXiv non-exclusive distribution license"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tran-2025-multi-agent-collaboration-mechanisms/"
---
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# Multi-Agent Collaboration Mechanisms: A Survey of LLMs

**Authors:** [Khanh-Tung Tran](https://md.research.elamur.ai/free/en/authors/khanh-tung-tran/), [Dung Dao](https://md.research.elamur.ai/free/en/authors/dung-dao/), Minh-Duong Nguyen, Quoc-Viet Pham, [Barry O'Sullivan](https://md.research.elamur.ai/free/en/authors/barry-o-sullivan/) and [Hoang D. Nguyen](https://md.research.elamur.ai/free/en/authors/hoang-d-nguyen/) · **Year:** 2025 · **Venue:** arXiv:2501.06322 · **Licence:** [arXiv non-exclusive distribution license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.06322) · [PDF](https://arxiv.org/pdf/2501.06322) · [DOI 10.48550/arxiv.2501.06322](https://doi.org/10.48550/arxiv.2501.06322) · [arXiv 2501.06322](https://arxiv.org/abs/2501.06322) · [OpenAlex](https://openalex.org/W6891925823)

**Citations:** 27 citations (OpenAlex, 2026-10-14) · [68](https://md.research.elamur.ai/free/en/papers/tran-2025-multi-agent-collaboration-mechanisms/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

Large language models still hallucinate, struggle with slow, deliberate reasoning and run into scaling limits. One response is to connect several LLM-based agents so they can split up work and reason together. Earlier surveys mostly covered single agents or touched on only one aspect of agent interaction, such as communication topology or ensembling. This survey, by authors from University College Cork, Pusan National University and Trinity College Dublin, puts collaboration itself at the centre. It reviews existing LLM-based multi-agent systems (MASs) and organizes them in a framework with five dimensions: the actors involved, the type of interaction, the structure of the communication network, the strategy that governs agent behaviour, and the coordination protocol. The authors also review deployments in several domains and close with open problems and lessons learned. The paper contributes no new dataset or model. Its value is a shared vocabulary and design map for building and comparing multi-agent setups. It also points out weak spots, such as error propagation between agents and fragmented evaluation practice, that matter for any team building agent pipelines.

## Key points

- **Framework dimensions:**
  - *Types:* cooperation, competition and coopetition.
  - *Structures:* peer-to-peer, centralized and distributed.
  - *Strategies:* role-based, rule-based and model-based.
- **Comparison with prior work:** Table 1 scores eight earlier surveys on four criteria. These are attention to multi-agent systems, depth on collaboration mechanisms, presence of a general framework, and coverage of applications. The authors rate their own work "High" on all four.
- **Application domains reviewed:** Industry 5.0 settings, 5G/6G communication networks, systems that answer questions, and simulations of social and cultural dynamics.
- **Open challenges:**
  - Hallucinations can cascade from one agent to others.
  - Adversarial attacks become more dangerous as the number of agents grows.
  - Scaling agent populations strains resources and coordination.
  - There is no standardized or dynamic benchmarking for MASs.
  - Human-like agents raise risks of deception and over-reliance by users.
- **Publication details:** arXiv:2501.06322 (v1, January 2025), 35 pages, funded by Science Foundation Ireland. The paper cites Gemini 2.0 as an example of a model built with agentic use in mind.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University College Cork](https://md.research.elamur.ai/free/en/universities/university-college-cork/) — University, Cork, Ireland · 6 works · 119 citations

Also on the byline: [Pusan National University](https://ror.org/01an57a31).

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## Cite

Cite as: Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O'Sullivan and Hoang D. Nguyen. 2025. Multi-Agent Collaboration Mechanisms: A Survey of LLMs. arXiv:2501.06322.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{tran2025multi,
  title = {{Multi-Agent Collaboration Mechanisms: A Survey of LLMs}},
  author = {Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O'Sullivan and Hoang D. Nguyen},
  year = {2025},
  doi = {10.48550/arxiv.2501.06322},
  eprint = {2501.06322},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.06322}
}
```

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## References

166 references, 54 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*