---
title: "LLM4SR — paper by Luo et al. (2025) — summary and ideas"
description: "The authors argue that earlier reviews were fragmented, focusing on model architectures, general… 198 references, cited by 34 papers here."
authors: ["Ziming Luo", "Zonglin Yang", "Zexin Xu", "Wei Yang", "Xinya Du"]
year: 2025
venue: "arXiv:2501.04306"
source: "https://arxiv.org/abs/2501.04306"
doi: "10.48550/arxiv.2501.04306"
arxiv: "2501.04306"
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/luo-2025-llm4sr-survey-large-language/"
---
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# LLM4SR: A Survey on Large Language Models for Scientific Research

**Authors:** [Ziming Luo](https://md.research.elamur.ai/free/en/authors/ziming-luo/), [Zonglin Yang](https://md.research.elamur.ai/free/en/authors/zonglin-yang/), Zexin Xu, [Wei Yang](https://md.research.elamur.ai/free/en/authors/wei-yang/) and [Xinya Du](https://md.research.elamur.ai/free/en/authors/xinya-du/) · **Year:** 2025 · **Venue:** arXiv:2501.04306 · **Licence:** [arXiv non-exclusive distribution license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.04306) · [PDF](https://arxiv.org/pdf/2501.04306) · [DOI 10.48550/arxiv.2501.04306](https://doi.org/10.48550/arxiv.2501.04306) · [arXiv 2501.04306](https://arxiv.org/abs/2501.04306) · [OpenAlex](https://openalex.org/W4406231286)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [34](https://md.research.elamur.ai/free/en/papers/luo-2025-llm4sr-survey-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This survey maps how large language models are being used across the full scientific research cycle. The authors argue that earlier reviews were fragmented, focusing on model architectures, general LLM skills, or a single stage. They instead organize the field around four stages: scientific hypothesis discovery, experiment planning and implementation, paper writing, and peer review. For each stage, they review methods, benchmarks and evaluation practices, then outline open problems and future directions. Hypothesis generation is traced back to two earlier lines of work, literature-based discovery and inductive reasoning. The survey contributes no new data or model. Its main result is a taxonomy linking dozens of systems, such as agents, writing assistants and review generators, to the research step they support, plus a public resource repository. Recurring weaknesses include hallucination, shallow domain understanding, poor coherence over long documents, scarce training data and ethical risks. The work matters as an entry point for anyone building or evaluating LLM tools that support research workflows.

## Key points
- **Scope and venue:** The authors describe it as the first systematic survey of LLMs for the whole research pipeline. It is published in ACM Computing Surveys (2025, 37 pages), with resources at github.com/du-nlp-lab/LLM4SR.
- **Historical roots of hypothesis discovery:** Swanson's literature-based discovery is a key precursor. Its "ABC" model proposes a link between concepts A and C when both co-occur with an intermediate concept B.
- **Systems and benchmarks by stage:**
  - Hypothesis discovery: SciMON, MOOSE, AIScientist and ResearchAgent, evaluated with DiscoveryBench and Tomato.
  - Experimentation: ChemCrow, Coscientist and CRISPR-GPT, evaluated with MLAgentBench, DS-1000 and ScienceAgentBench.
  - Writing: AutoSurvey and LitLLM, evaluated with ALCE and CiteBench.
  - Peer review: ReviewRobot, MARG and CycleReviewer, evaluated with PeerRead, NLPeer and ASAP-Review.
- **Peer-review challenges:** Current models miss methodological flaws such as weak statistics or missing controls. They also hallucinate assessments and lack training data in smaller fields. Ethical risks include bias, "plagiarism laundering" and homogenized feedback.
- **Stated limitation:** The survey covers only LLMs within the broader "AI for Science" area. It may miss relevant work published outside computer science venues.

*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

- [The University of Texas at Dallas](https://md.research.elamur.ai/free/en/universities/the-university-of-texas-at-dallas/) — University, Richardson, United States · 66 works · 5,836 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,959 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,789 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,621 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 4,145 works

## Cite

Cite as: Ziming Luo, Zonglin Yang, Zexin Xu, Wei Yang and Xinya Du. 2025. LLM4SR: A Survey on Large Language Models for Scientific Research. arXiv:2501.04306.

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

```bibtex
@misc{luo2025llm4sr,
  title = {{LLM4SR: A Survey on Large Language Models for Scientific Research}},
  author = {Ziming Luo, Zonglin Yang, Zexin Xu, Wei Yang and Xinya Du},
  year = {2025},
  doi = {10.48550/arxiv.2501.04306},
  eprint = {2501.04306},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.04306}
}
```

- [← previous From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://md.research.elamur.ai/free/en/papers/zheng-2025-automation-autonomy-survey-large/)
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- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/3/)

## References

198 references, 92 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 3 November 2026*