---
title: "AI4Research — paper by Chen et al. (2025) — summary and ideas"
description: "The paper proposes no new model. 960 references, cited by 17 papers here. Among them: 4."
authors: ["Qiguang Chen", "Mingda Yang", "Libo Qin", "Jinhao Liu", "Zheng Yan", "Jiannan Guan", "Dengyun Peng", "Yiyan Ji", "Hanjing Li", "Mengkang Hu", "Yimeng Zhang", "Yihao Liang", "Yuhang Zhou", "Jiaqi Wang", "Zhi Chen", "Wanxiang Che"]
year: 2025
venue: "arXiv:2507.01903"
source: "https://arxiv.org/abs/2507.01903"
doi: "10.48550/arxiv.2507.01903"
arxiv: "2507.01903"
licence: "CC BY-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/)

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# AI4Research: A Survey of Artificial Intelligence for Scientific Research

**Authors:** [Qiguang Chen](https://md.research.elamur.ai/free/en/authors/qiguang-chen/), Mingda Yang, [Libo Qin](https://md.research.elamur.ai/free/en/authors/libo-qin/), [Jinhao Liu](https://md.research.elamur.ai/free/en/authors/jinhao-liu/), Zheng Yan, [Jiannan Guan](https://md.research.elamur.ai/free/en/authors/jiannan-guan/), [Dengyun Peng](https://md.research.elamur.ai/free/en/authors/dengyun-peng/), Yiyan Ji, Hanjing Li, [Mengkang Hu](https://md.research.elamur.ai/free/en/authors/mengkang-hu/), [Yimeng Zhang](https://md.research.elamur.ai/free/en/authors/yimeng-zhang/), Yihao Liang, [Yuhang Zhou](https://md.research.elamur.ai/free/en/authors/yuhang-zhou/), [Jiaqi Wang](https://md.research.elamur.ai/free/en/authors/jiaqi-wang/), [Zhi Chen](https://md.research.elamur.ai/free/en/authors/zhi-chen/) and [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/) · **Year:** 2025 · **Venue:** [arXiv:2507.01903](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/) ([Science China Information Sciences 2025](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/2025/)) · **Publisher:** [Springer Nature](https://md.research.elamur.ai/free/en/publishers/springer-nature/) · **Licence:** [CC BY-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2507.01903) · [PDF](https://arxiv.org/pdf/2507.01903) · [DOI 10.48550/arxiv.2507.01903](https://doi.org/10.48550/arxiv.2507.01903) · [arXiv 2507.01903](https://arxiv.org/abs/2507.01903) · [OpenAlex](https://openalex.org/W4417183323)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [17](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Reasoning-focused LLMs such as OpenAI-o1 and DeepSeek-R1 have prompted many efforts to automate parts of scientific work. Until now, no single overview connected these efforts. This survey, led by a team at Harbin Institute of Technology, fills that gap by mapping AI support across the entire research lifecycle, not just discovery. The authors group prior work into five task families:

- understanding papers, including text, tables and charts;
- generating literature surveys;
- discovery itself, covering ideation, novelty assessment, theory checking, experiments and end-to-end systems;
- manuscript writing;
- peer review before, during and after evaluation.

They also review domain applications in the natural sciences, engineering and the social sciences. They catalogue datasets and tools and outline open problems such as ethics, explainability, real-time experimentation, multimodality and multilingual support. The paper proposes no new model. Its contribution is a taxonomy plus a curated resource list. For teams working with lower-resource languages like Ukrainian, the multilingual section is especially useful. It identifies terminology alignment and uneven cross-lingual performance as key obstacles.

## Key points
- The taxonomy rests on five pillars: comprehending scientific content, surveying the academic literature, making scientific discoveries, producing academic writing, and conducting academic peer review. Each pillar is split into semi- and fully automatic settings or into pre-, in- and post-review stages.
- Reference systems discussed include the AI Scientist, which runs idea mining, experiments and writing and produces a LaTeX report with automated review. Others are Carl, Zochi, multi-agent AgentLab and AgentArxiv, and the Virtual Lab, which designed nanobody binders for SARS-CoV-2 variants.
- Application chapters cover physics, biology and medicine, chemistry and materials, robotics, software engineering, sociology and psychology.
- Seven future directions are listed. They range from interdisciplinary models and ethics to multilingual integration, where the main challenges are capacity versus language coverage and preserving academic rhetoric across languages.
- Resources are available at github.com/LightChen233/Awesome-AI4Research and ai-4-research.github.io.

*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 of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 184 works · 69,372 citations
- [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 167 works · 20,987 citations
- [Princeton University](https://md.research.elamur.ai/free/en/universities/princeton-university/) — University, Princeton, United States · 90 works · 39,109 citations
- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 158 works · 28,841 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
- [University of Hong Kong](https://md.research.elamur.ai/free/en/universities/university-of-hong-kong/) — University, Pok Fu Lam, China · 89 works · 11,774 citations
- [Central South University](https://md.research.elamur.ai/free/en/universities/central-south-university/) — University, Changsha, China · 15 works · 582 citations

📚 Shelves (6): [🖥️ 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 · [🎯 Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · 209 works · [🎯 Information Systems and Management](https://md.research.elamur.ai/free/en/shelves/decision-sciences/information-systems-and-management/) · 41 works · [🧱 Materials Science](https://md.research.elamur.ai/free/en/shelves/materials-science/) · 26 works · [🧱 Materials Chemistry](https://md.research.elamur.ai/free/en/shelves/materials-science/materials-chemistry/) · 26 works

💡 Concepts (1): [💡 Artificial intelligence](https://md.research.elamur.ai/free/en/concepts/artificial-intelligence/) · 49 works

## Cite

Cite as: Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, Yimeng Zhang, Yihao Liang, Yuhang Zhou, Jiaqi Wang, Zhi Chen and Wanxiang Che. 2025. AI4Research: A Survey of Artificial Intelligence for Scientific Research. arXiv:2507.01903.

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

```bibtex
@misc{chen2025ai4research,
  title = {{AI4Research: A Survey of Artificial Intelligence for Scientific Research}},
  author = {Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, Yimeng Zhang, Yihao Liang, Yuhang Zhou, Jiaqi Wang, Zhi Chen and Wanxiang Che},
  year = {2025},
  doi = {10.48550/arxiv.2507.01903},
  eprint = {2507.01903},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2507.01903}
}
```

- [← previous Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home](https://md.research.elamur.ai/free/en/papers/moskvoretskii-2025-adaptive-retrieval-without-self-knowledge/)
- [next → Can Language Models Reason about Individualistic Human Values and Preferences?](https://md.research.elamur.ai/free/en/papers/jiang-2025-can-language-models-reason-about/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/4/)

## References

960 references, 108 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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956. Zhenzhen Zhuang, Jiandong Chen, Hongfeng Xu, Yuwen Jiang, and Jialiang Lin. Large language models for automated scholarly paper review: A survey. arXiv preprint arXiv:2501.10326, 2025. — [arXiv:2501.10326](https://arxiv.org/abs/2501.10326) [find](https://scholar.google.com/scholar?q=Zhenzhen+Zhuang%2C+Jiandong+Chen%2C+Hongfeng+Xu%2C+Yuwen+Jiang%2C+and+Jialiang+Lin%2E+Large+language+models+for+automated+scholarly+paper+review%3A+A+survey%2E+arXiv+preprint+arXiv%3A2501%2E10326%2C+2025%2E+%E2%80%94+%5BarXiv%3A2501%2E1)
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960. Apostolos Zournas, Matthew R Incha, Tijana Radivojevic, Vincent Blay, Jose Manuel Martí, Zak Costello, Matthias Schimdt, Tan Chung, Mitchell G Thompson, Allison Pearson, et al. Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in pseudomonas putida.… [find](https://scholar.google.com/scholar?q=Apostolos+Zournas%2C+Matthew+R+Incha%2C+Tijana+Radivojevic%2C+Vincent+Blay%2C+Jose+Manuel+Mart%C3%AD%2C+Zak+Costello%2C+Matthias+Schimdt%2C+Tan+Chung%2C+Mitchell+G+Thompson%2C+Allison+Pearson%2C+et+al%2E+Machine+learning%2Dled+se)

*Updated 31 October 2026*