Hacker News AI · 2026/10/6 22:17:21
OpenAI 发布前沿模型数学成果并开源 Lean 证明
OpenAI 通过 GitHub 仓库公开了其内部前沿模型产生的一系列新数学结果，并遵循普林斯顿高等研究院顾问组的建议建立了论文修订与引用规范。此次发布不仅包含预印本，还重点提供了大量基于 Lean 语言的计算机可验证形式化证明，旨在提升 AI 在纯数学领域产出的可信度与社区接受度。
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We’re releasing a broad range of new mathematical results produced by an internal frontier model.
As we look to improve how we share results with the math community, we’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study(opens in a new window) to develop best practices, and we have drawn on their advice and public recommendations(opens in a new window) to inform how we release these results.
For this release, we’re publishing the results in a GitHub repository, with protocols for paper revisions and citations. We’re continuing to explore other community-hosted alternatives for this release which meet the committee’s guidelines. For future releases, we are committed to further improving the quality of the papers via the citations, mathematical exposition, and presentation of the results for better understanding.
As part of our GitHub repository, we are sharing formalizations of many of the proofs in Lean, a programming language that allows mathematical proofs to be checked by a computer. We will update the repository with more formalizations as we obtain them.
To promote scientific transparency and openness, we are also publishing additional details about how we obtained the results in the repository. These include 10 summaries of the model’s reasoning, estimations of compute spent in terms of Pro usage on ChatGPT, and statistics about the number of attempted problems. The average result used the equivalent compute of roughly three hours of ChatGPT Pro thinking.
We want this progress to push the frontier of human knowledge and enable further progress in mathematics. We will be funding a series of workshops, conferences, and special programs around the understanding of major results produced by AI—we will share more on this in the near future.
We want to directly empower scientists with state-of-the-art capabilities and are working to responsibly release the model that produced these results. This is why it is important to continue to evaluate our internal frontier models on mathematics and other sciences, so we can accelerate developing the tools to advance those fields. We will continue to act on feedback from the community and update our standards for future disclosures of major scientific advancements.
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