Early science acceleration experiments with GPT-5

• URL: http://arxiv.org/abs/2511.16072v1
• Date: Thu, 20 Nov 2025 06:04:23 GMT
• Title: Early science acceleration experiments with GPT-5
• Authors: 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,
• Abstract summary: We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research.<n>In these examples, the authors highlight how AI accelerated their work, and where it fell short.<n>We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI.
• Score: 58.27301147653905
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Accelerating Scientific Research with Gemini: Case Studies and Common Techniques [105.15622072347811]
  Large language models (LLMs) have opened new avenues for accelerating scientific research.<n>We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models.
  arXiv  Detail & Related papers  (2026-02-03T18:56:17Z)
• Solving a Research Problem in Mathematical Statistics with AI Assistance [19.35055637720468]
  We show how GPT-5 helped us solve a previously unsolved research problem in robust mathematical statistics.<n>Our problem concerns robust density estimation, where the observations are perturbed by Wasserstein-bounded contaminations.<n>GPT-5 provided crucial help along the way, including by suggesting calculations that we did not think of, and techniques that were not familiar to us.
  arXiv  Detail & Related papers  (2025-11-24T07:03:56Z)
• Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future? [20.188659973082643]
  Sakana recently introduced the 'AI Scientist', claiming to conduct research autonomously, i.e. they imply to have achieved what we term Artificial Research Intelligence (ARI)<n>Our evaluation of the AI Scientist reveals critical shortcomings.
  arXiv  Detail & Related papers  (2025-02-20T06:22:03Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Formal Mathematical Reasoning: A New Frontier in AI [60.26950681543385]
  We advocate for formal mathematical reasoning and argue that it is indispensable for advancing AI4Math to the next level.<n>We summarize existing progress, discuss open challenges, and envision critical milestones to measure future success.
  arXiv  Detail & Related papers  (2024-12-20T17:19:24Z)
• AI Expands Scientists' Impact but Contracts Science's Focus [15.771625061196927]
  We show accelerated adoption of AI among scientists and consistent professional advantages associated with AI use.<n>Scientists who engage in AI-augmented research publish 3.02 times more papers, receive 4.84 times more citations.<n>By contrast, AI research shrinks the collective volume of scientific topics studied by 4.63%.
  arXiv  Detail & Related papers  (2024-12-10T18:24:17Z)
• Sparks of Artificial General Intelligence: Early experiments with GPT-4 [66.1188263570629]
  GPT-4, developed by OpenAI, was trained using an unprecedented scale of compute and data. We demonstrate that GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more. We believe GPT-4 could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.
  arXiv  Detail & Related papers  (2023-03-22T16:51:28Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.