---
title: "Scaling Large Language Model-based Multi-Agent Collaboration"
description: "The authors propose MacNet, which places agents on a directed acyclic graph. 107 references, cited by 63 papers here."
authors: ["Chen Qian", "Zihao Xie", "YiFei Wang", "Wei Liu", "Kunlun Zhu", "Hanchen Xia", "Yufan Dang", "Zhuoyun Du", "Weize Chen", "Cheng Yang", "Zhiyuan Liu", "Maosong Sun"]
year: 2024
venue: "arXiv:2406.07155"
source: "https://arxiv.org/abs/2406.07155"
doi: "10.48550/arxiv.2406.07155"
arxiv: "2406.07155"
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/qian-2024-scaling-large-language-model/"
---
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# Scaling Large Language Model-based Multi-Agent Collaboration

**Authors:** [Chen Qian](https://md.research.elamur.ai/free/en/authors/chen-qian/), Zihao Xie, [YiFei Wang](https://md.research.elamur.ai/free/en/authors/yifei-wang/), [Wei Liu](https://md.research.elamur.ai/free/en/authors/wei-liu/), [Kunlun Zhu](https://md.research.elamur.ai/free/en/authors/kunlun-zhu/), [Hanchen Xia](https://md.research.elamur.ai/free/en/authors/hanchen-xia/), [Yufan Dang](https://md.research.elamur.ai/free/en/authors/yufan-dang/), [Zhuoyun Du](https://md.research.elamur.ai/free/en/authors/zhuoyun-du/), [Weize Chen](https://md.research.elamur.ai/free/en/authors/weize-chen/), [Cheng Yang](https://md.research.elamur.ai/free/en/authors/cheng-hong-yang/), [Zhiyuan Liu](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/) and [Maosong Sun](https://md.research.elamur.ai/free/en/authors/maosong-sun/) · **Year:** 2024 · **Venue:** arXiv:2406.07155 · **Licence:** [arXiv non-exclusive distribution license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2406.07155) · [PDF](https://arxiv.org/pdf/2406.07155) · [DOI 10.48550/arxiv.2406.07155](https://doi.org/10.48550/arxiv.2406.07155) · [arXiv 2406.07155](https://arxiv.org/abs/2406.07155) · [OpenAlex](https://openalex.org/W4399597845)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [63](https://md.research.elamur.ai/free/en/papers/qian-2024-scaling-large-language-model/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most LLM-based multi-agent systems use fewer than ten agents, so it has been unclear whether adding far more collaborators keeps improving results, as adding neurons does under neural scaling laws. The authors propose MacNet, which places agents on a directed acyclic graph. An actor on each node produces or revises an artifact, such as code, an answer or a story, and a critic on each edge tells it what to improve. Agents run in topological order, with a multi-turn critic–actor exchange on each edge. Only the final artifact is passed downstream, not the dialogue history, which keeps context growth in check. Nodes with several parents first merge the incoming artifacts. Across several downstream tasks and chain, tree and graph layouts, MacNet beats baselines on average and works with over a thousand agents. Irregular topologies outperform regular ones. Performance grows logistically with agent count, a "collaborative scaling law," and emergence appears earlier than in neural scaling. This points to inference-time collaboration as a way to gain quality without retraining.

## Key points
- MacNet tests six topologies in three families:
  - **Chain**
  - **Tree:** star (wide) and tree (deep)
  - **Graph:** fully connected mesh, MLP-like layered, and random
- A network with node set V and edge set E uses |V|+|E| agents and needs at least 2×|E| interaction rounds. Traversal follows topological ordering (Kahn, 1962), so a node runs only after all its predecessors.
- Memory has two parts: short-term memory covers the current dialogue, and long-term memory keeps only finished artifacts. This prevents context explosion and makes networks of more than 1,000 agents feasible.
- Performance follows a logistic curve as agents are added, and random (irregular) graphs beat regular structures. The authors suggest that more agents bring more perspectives into reflection and refinement.
- Code is available at github.com/OpenBMB/ChatDev (branch `macnet`). The experimental section was cut from the provided text, so benchmark names and scores are not included here.

*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

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 415 works · 94,028 citations
- [Peng Cheng Laboratory](https://md.research.elamur.ai/free/en/labs/peng-cheng-laboratory/) — Research institute, Shenzhen, China · 47 works · 2,699 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

## Cite

Cite as: Chen Qian, Zihao Xie, YiFei Wang, Wei Liu, Kunlun Zhu, Hanchen Xia, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Zhiyuan Liu and Maosong Sun. 2024. Scaling Large Language Model-based Multi-Agent Collaboration. arXiv:2406.07155.

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

```bibtex
@misc{qian2024scaling,
  title = {{Scaling Large Language Model-based Multi-Agent Collaboration}},
  author = {Chen Qian, Zihao Xie, YiFei Wang, Wei Liu, Kunlun Zhu, Hanchen Xia, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Zhiyuan Liu and Maosong Sun},
  year = {2024},
  doi = {10.48550/arxiv.2406.07155},
  eprint = {2406.07155},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2406.07155}
}
```

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## References

107 references, 49 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*