Scientific production in the era of Large Language Models

Summary

The headline Science study on LLMs and research output: 2.1M preprints (arXiv, bioRxiv, SSRN), 28K peer review reports and 246M document accesses. Reports 23.7-89.3% higher publication rates for LLM-adopting scientists depending on field and author background, a decoupling of linguistic complexity from substance ('linguistically complex but substantively underwhelming'), and broader engagement with monographs and newer, underused sources. Must be cited together with the PNAS rebuttal below, which disputes the causal identification.

Contribution

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Method

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Relevance to RISE

Surfaced by the 2026-08-29 literature sweep as a high-priority candidate for the RISE knowledge base (angle: research productivity of AI). Verified against: arXiv abstract page opened 2026-08-29 (6 authors, journal ref Science 390(6779):1240-1243, full abstract read); Semantic Scholar publisher record for the Science DOI also opened

Critique / open questions

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Key quotes

Large Language Models (LLMs) are rapidly reshaping scientific research. (abstract)

We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. (abstract)