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

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.