---
title: "Many Heads Are Better Than One — paper by Su et al. (2025)"
description: "The authors build a \"Scientific Research Ecosystem\", a snapshot of a research community at a… 76 references, cited by 27 papers here."
authors: ["Haoyang Su", "Renqi Chen", "Shixiang Tang", "Zhenfei Yin", "Xinzhe Zheng", "Jinzhe Li", "Biqing Qi", "Qi Wu", "Hui Li", "Wanli Ouyang", "Philip Torr", "Bowen Zhou", "Nanqing Dong"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.1368/"
doi: "10.18653/v1/2025.acl-long.1368"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/su-2025-many-heads-are-better-than/"
---
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# Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System

**Authors:** Haoyang Su, Renqi Chen, Shixiang Tang, [Zhenfei Yin](https://md.research.elamur.ai/free/en/authors/zhenfei-yin/), Xinzhe Zheng, Jinzhe Li, [Biqing Qi](https://md.research.elamur.ai/free/en/authors/biqing-qi/), [Qi Wu](https://md.research.elamur.ai/free/en/authors/qi-wu/), [Hui Li](https://md.research.elamur.ai/free/en/authors/hui-li/), [Wanli Ouyang](https://md.research.elamur.ai/free/en/authors/wanli-ouyang/), [Philip Torr](https://md.research.elamur.ai/free/en/authors/philip-h-s-torr/), [Bowen Zhou](https://md.research.elamur.ai/free/en/authors/bowen-zhou/) and Nanqing Dong · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.1368/) · [PDF](https://aclanthology.org/2025.acl-long.1368.pdf) · [DOI 10.18653/v1/2025.acl-long.1368](https://doi.org/10.18653/v1/2025.acl-long.1368) · [OpenAlex](https://openalex.org/W4412889809)

**Citations:** 13 citations (OpenAlex, 2026-10-14) · [27](https://md.research.elamur.ai/free/en/papers/su-2025-many-heads-are-better-than/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most LLM tools for automated research ideation use a single agent or rely on artificial collaboration setups, such as invented researcher profiles or synthetic co-author networks. This paper asks whether a team of LLM agents grounded in real scholarly data can produce more novel ideas. The authors build a "Scientific Research Ecosystem", a snapshot of a research community at a chosen date. It contains agents cloned from real scientists' backgrounds and publications, a database of papers from before the cutoff, and a database of papers from after it.

On top of this ecosystem they propose VirSci, a five-stage pipeline:
1. A leader agent picks collaborators.
2. The team agrees on a topic.
3. Members propose and refine ideas, with discussion inside the team and with invited outside agents.
4. Candidate ideas are checked for novelty against existing literature and voted on.
5. The chosen idea is written up as an abstract.

Generated abstracts are scored by comparing them to the past and contemporary paper databases. VirSci beats a single-agent setup and the prior state of the art. It also reproduces known Science of Science patterns, which makes it a possible testbed for studying collaboration.

## Key points
- **Evaluation metrics:** three novelty measures cover distance from earlier work (Historical Dissimilarity), fit with emerging research trends (Contemporary Dissimilarity), and likely influence on contemporary research (CI). They are combined into Overall Novelty (ON).
- **Main result:** compared with a single agent, the multi-agent system improves alignment with contemporary research by +13.8% on average and potential impact by +44.1%.
- **Data:** experiments use two public AMiner datasets, one single-discipline and one multi-discipline (Open Academic Graph). Scientists' names are masked for privacy.
- **Team diversity:** novelty follows an inverted-U curve, peaking at roughly 50–75% diversity. Fresher teams also tend to produce more innovative ideas, consistent with Science of Science findings.
- **Code and limitations:** code is released at github.com/open-sciencelab/Virtual-Scientists. The authors note that the system cannot model researchers working in several teams at once, inherits LLM biases, and could generate plausible but incorrect claims.

*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 · 333 works · 87,019 citations
- [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) — University, Oxford, United Kingdom · 104 works · 157,183 citations
- [Chinese University of Hong Kong](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) — University, Hong Kong, China · 126 works · 14,073 citations
- [Shanghai Artificial Intelligence Laboratory](https://md.research.elamur.ai/free/en/labs/shanghai-artificial-intelligence-laboratory/) — Research institute, Shanghai, China · 117 works · 11,281 citations
- [Shanghai Innovation Institute](https://md.research.elamur.ai/free/en/universities/shanghai-innovation-institute/) — University, Shanghai, China · 24 works · 262 citations

Also on the byline: [Shanghai Institute for Science of Science](https://ror.org/004srxn73).

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works

## Cite

Cite as: Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou and Nanqing Dong. 2025. Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{su2025many,
  title = {{Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System}},
  author = {Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou and Nanqing Dong},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1368},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1368}
}
```

- [← previous MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale](https://md.research.elamur.ai/free/en/papers/guo-2025-mammoth-vl-eliciting-multimodal-reasoning/)
- [next → Towards Robust Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/luong-2025-robust-mathematical-reasoning/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/2/)

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*Updated 31 October 2026*