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arXiv:2511.16072cs.CL

Early science acceleration experiments with GPT-5

Sébastien Bubeck, Christian Coester, Ronen Eldan, Timothy Gowers, Yin Tat Lee, Alexandru Lupsasca, Mehtaab Sawhney, Robert Scherrer, Mark Sellke, Brian K. Spears, Derya Unutmaz, Kevin Weil, Steven Yin, Nikita Zhivotovskiy

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing.

arXiv comments
89 pages
Journal reference
DOI
Subjects
cs.CLComputation and Language cs.AIArtificial Intelligence

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jan 2026 to Jan 2026. Found in 1 HN submission from Nov 2025 to Nov 2025. Open submission discussions.

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    • Formalized the Sawhney-Sellke stability theorem for Erdős Problem #848 (squarefree products) in Lean 4. ~3800 lines, compiles with 0 errors, no sorries, no axioms. The math was solved by Sawhney & Sellke (Nov 2025): https://arxiv.org/abs/2511.16072 Only ~17 Erdős problems have…

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