OpenAI dumps 372 AI-generated math proofs on GitHub, telling the academic world to keep up

OpenAI has published 372 mathematical results generated by an internal frontier model on GitHub rather than in academic journals. Each result is intended to solve an open problem or make substantial progress toward one. The collection includes improvements to major computer algorithms and advances related to the Riemann hypothesis.

The results are hosted with revision logs and citations. According to OpenAI, the same model already produced a solution to a Navier-Stokes problem that has been under formal review for weeks. Nearly every result came from a single prompt to a single AI agent, though some took multiple attempts, a contrast with the Navier-Stokes solution, which required a swarm of 10,000 agents and millions of dollars in compute. On average, each result consumed roughly three hours of ChatGPT Pro Thinking compute.

Many proofs come with formalizations in Lean, a language built for machine-checkable proofs, with more planned. OpenAI also published methodology details including reasoning summaries, problem-attempt statistics, and compute cost estimates.

OpenAI consulted the Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study and loosely followed its public recommendations, having set a boundary that the mathematicians can advise on communication but not on whether or how fast results are produced. The group includes Fields Medal winner Timothy Gowers. OpenAI announced plans to fund workshops and conferences on understanding AI-produced results, and said it is working on a responsible model release to "directly empower scientists with state-of-the-art capabilities."

Lean formalizations can verify logical correctness but cannot judge mathematical relevance or originality. Reactions range from excitement to frustration. In an open letter titled "A Severe Misalignment of AI in Mathematics," 25 Fields Medal winners warned of a disconnect between AI industry goals and mathematics, arguing that problem-solving is a tool and proxy for conceptual understanding and insight, and that mass-producing true statements could destroy fertile ground rather than bring new ideas to life. Gowers has warned that within one to two decades mathematical literature could grow enormously while no human community truly understands it. Terence Tao has said training young mathematicians should emphasize the human side and tightly limit AI tool use.