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Major Revision, Minor Accountability: LLMs as Reviewers of Published Articles in Top-Tier Journals

Bastien Le Guellec, Su Hwan Kim, Raphaël Bentegeac, Aghilès Hamroun · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31414 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

専門的な科学的出力の増加に伴い、ピアスレビューシステムはシグナルブレイクポイントに近づいている:レビュー者はますます確保しにくくなり、回転時間はそれに応じて長くなります。 LLM助成またはLLM生成のピアスレビューは治療法として提案されていますが、このようなモデルは、現在のシステムが科学的有効性のゴールドスタンダードに最も近いもので、すでにトップレベルのジャーナルに掲載されている手書きをどのように扱うかは不明です。ここでは、私たちは、CC BY 4.0特有のライセンスの下でそれぞれリリースされ、2026年に2026年に、20の一般科学、ライフサイエンス、および臨床医学ジャーナルに含まれる20のレコードから

Machine translation (Meta M2M100); the English original follows.

Abstract

With the growth of scientific output, the peer review system is approaching a breaking point: reviewers are increasingly hard to secure, and turnaround times lengthen accordingly. LLM-assisted or LLM-generated peer review has been proposed as a remedy, but how such models would treat manuscripts already published in top-tier journals, the closest thing the current system has to a gold standard of scientific validity, is not known. Here we assembled a corpus of 200 articles, each released under a CC BY 4.0 license and published in 2026, from 20 general-science, life-science, and clinical-medicine journals including Nature, Science, and Cell, and asked four current LLMs (two proprietary, two open-weight) to produce independent peer-review reports of each one under a blinded protocol. Their decisions were overwhelmingly harsh and starkly inconsistent with each other: 605 of 800 reviews (75.6%) recommended major revision or rejection, but the rate at which any single model did so ranged from 17.5% to 100% (p < 0.001), disagreement on manuscripts that had, by construction, already survived real editorial review at the journals in question. When the same manuscripts were doctored with one deliberately planted, field-specific methodological or statistical flaw, detection rates varied from 17.0% to 60.5% across models (p < 0.001). Recommending revision for papers that had already passed editorial review (low specificity) while missing flaws deliberately placed in front of them (low sensitivity), these models produce output that should be read for what it is: an uneven, framing- and model-dependent signal.

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Topics: Minor (academic), Publishing, License, Impact factor, History, Original research

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