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New dataset FIRSTPASS trains AI on multidisciplinary scientific peer review

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced FIRSTPASS, a new dataset designed to train AI systems on scientific peer review across multiple disciplines. Unlike previous datasets limited to computer science, FIRSTPASS includes full editorial dialogues from Nature Communications, covering biology, chemistry, neuroscience, physics, and earth science. The dataset contains 3,668 records with outcomes derived from editorial decisions, offering a more comprehensive and realistic benchmark for AI's ability to assess scientific validity. AI

IMPACT This dataset could enable AI models to better understand and evaluate scientific research across diverse fields, improving AI's capacity for critical analysis.

RANK_REASON The item describes a new dataset for AI research, released on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset FIRSTPASS trains AI on multidisciplinary scientific peer review

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The item describes a new dataset for AI research, released on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Prabhjot Singh, Somnath Luitel, Manmeet Singh, Josh Durkee ·

    FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes

    arXiv:2608.26129v1 Announce Type: cross Abstract: Scientific peer review datasets have trained AI systems exclusively on Computer Science and Machine Learning venues, producing models that critique ablation studies yet have never seen a biology reviewer demand contamination contr…

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