When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification
  of Scientific Research
Recent advances in large language models (LLMs) have fueled the vision of
automated scientific discovery, often called AI Co-Scientists. To date, prior
work casts these systems as generative co-authors responsible for crafting
hypotheses, synthesizing code, or drafting manuscripts. In this work, we
explore a complementary application: using LLMs as verifiers to automate the
academic verification of scientific manuscripts. To that end, we
introduce SPOT, a dataset of 83 published papers paired with 91 errors
significant enough to prompt errata or retraction, cross-validated with actual
authors and human annotators. Evaluating state-of-the-art LLMs on SPOT, we find
that none surpasses 21.1\% recall or 6.1\% precision (o3 achieves the best
scores, with all others near zero). Furthermore, confidence estimates are
uniformly low, and across eight independent runs, models rarely rediscover the
same errors, undermining their reliability. Finally, qualitative analysis with
domain experts reveals that even the strongest models make mistakes resembling
student-level misconceptions derived from misunderstandings. These findings
highlight the substantial gap between current LLM capabilities and the
requirements for dependable AI-assisted academic verification.
682c11fe08d047591841ebf1
Guijin Son
admin_assigned
2025-05-20T08:40:55.774Z
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GUIJIN SON
user
amphora
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Jiwoo Hong
admin_assigned
2025-05-20T08:41:14.541Z
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Jiwoo Hong
user
JW17
682c11fe08d047591841ebf3
Honglu Fan
682c11fe08d047591841ebf4
Heejeong Nam
admin_assigned
2025-05-20T08:41:30.411Z
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Heejeong Nam
user
HazelNam
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Hyunwoo Ko
claimed_verified
2025-05-20T07:20:13.177Z
63e087b6a98d931aa90c1b9c
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Hyunwoo Ko
user
Cartinoe5930
682c11fe08d047591841ebf6
Seungwon Lim
admin_assigned
2025-05-20T08:41:49.745Z
63be1cd13b0665ad51d29c37
/avatars/5acc9b9bbecac3d567e927e2d8667b00.svg
Seungwon Lim
user
sngwon
682c11fe08d047591841ebf7
Jinyeop Song
682c11fe08d047591841ebf8
Jinha Choi
682c11fe08d047591841ebf9
Gonçalo Paulo
682c11fe08d047591841ebfa
Youngjae Yu
682c11fe08d047591841ebfb
Stella Biderman
claimed_verified
2025-06-07T05:49:57.112Z
60347d3660e3dd96631c9093
https://cdn-avatars.huggingface.co/v1/production/uploads/60347d3660e3dd96631c9093/B3fuZer5N04tZIAYrLnz4.jpeg
Stella Biderman
user
stellaathena
When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification
  of Scientific Research
Recent advances in large language models (LLMs) have fueled the vision of
automated scientific discovery, often called AI Co-Scientists. To date, prior
work casts these systems as generative co-authors responsible for crafting
hypotheses, synthesizing code, or drafting manuscripts. In this work, we
explore a complementary application: using LLMs as verifiers to automate the
academic verification of scientific manuscripts. To that end, we
introduce SPOT, a dataset of 83 published papers paired with 91 errors
significant enough to prompt errata or retraction, cross-validated with actual
authors and human annotators. Evaluating state-of-the-art LLMs on SPOT, we find
that none surpasses 21.1\% recall or 6.1\% precision (o3 achieves the best
scores, with all others near zero). Furthermore, confidence estimates are
uniformly low, and across eight independent runs, models rarely rediscover the
same errors, undermining their reliability. Finally, qualitative analysis with
domain experts reveals that even the strongest models make mistakes resembling
student-level misconceptions derived from misunderstandings. These findings
highlight the substantial gap between current LLM capabilities and the
requirements for dependable AI-assisted academic verification.
Evaluation of LLMs on an academic manuscript verification dataset (SPOT) shows poor recall, precision, and reliability, indicating significant limitations in current AI's ability to replace human verification in scientific research.
large language models LLMs AI Co-Scientists generative co-authors academic verification of scientific manuscripts SPOT confidence estimates qualitative analysis