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home / papers / 2025 / When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research

When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research

Paper · 2025 · Guijin Son, Jiwoo Hong, Honglu Fan et al.

Summary

Large language models are increasingly promoted as "AI Co-Scientists" that generate hypotheses, code, and manuscripts. Far less is known about whether they can check scientific work for mistakes. The authors address this gap with SPOT (Scientific Paper Error Detection), a multimodal benchmark of 83 papers containing 91 errors serious enough to have triggered an erratum or a retraction.

The papers were drawn from WithdrarXiv and PubPeer and published in 2024 or later, which reduces the risk of training-data contamination. GPT-4o filtered the candidates, and only errors the original authors explicitly acknowledged were kept. Two independent groups of human annotators then audited the selection. Each paper was converted into Markdown text plus images using Llama-Parse, followed by a GPT-4.1 correction pass and a manual check.

When frontier models were tested on SPOT, the best one, o3, reached only about 21% recall and 6% precision. Models also rarely found the same errors across repeated runs. Expert case studies traced the failures to gaps in long-tail domain knowledge and to unstated multi-step calculations. The results suggest current LLMs cannot yet be relied on to verify scientific research.

Key points

  • SPOT contains 83 manuscripts with 91 errors across ten fields, including Mathematics, Physics, Biology, Materials Science, and Computer Science. Of these errors, 59 led to errata and 32 to retractions.
  • The six error categories are Equation/proof (37), Figure duplication (27), Data inconsistency (18), Statistical reporting (4), Reagent identity (3), and Experiment setup (2).
  • Inputs are long: an average of roughly 12,900 tokens (GPT-4o tokenizer) and 17.5 images per paper.
  • Pass@1 scores were 18.4% for o3, 7.3% for Gemini 2.5 Pro, and 0.9% for Llama-4-Maverick. Confidence was low, and models seldom rediscovered the same errors across eight runs.
  • The metadata is released on Hugging Face as amphora/SPOT-MetaData.

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

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksDecision Sciences · 306 worksInformation Systems and Management · 96 worksHistory and Philosophy of Science · 83 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1,394 works

Cite

Cite as Guijin Son, Jiwoo Hong, Honglu Fan, Heejeong Nam, Hyunwoo Ko, Seungwon Lim, Jinyeop Song, Jinha Choi, Gonçalo Paulo, Youngjae Yu and Stella Biderman. 2025. When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research. arXiv:2505.11855.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2505.11855 · arXiv 2505.11855

@misc{son2025when,
  title = {{When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research}},
  author = {Guijin Son, Jiwoo Hong, Honglu Fan, Heejeong Nam, Hyunwoo Ko, Seungwon Lim, Jinyeop Song, Jinha Choi, Gonçalo Paulo, Youngjae Yu and Stella Biderman},
  year = {2025},
  doi = {10.48550/arxiv.2505.11855},
  eprint = {2505.11855},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.11855}
}

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