Advisory Group on Mathematics and Artificial Intelligence
OpenAI has been training a new internal model since August 28, which has successfully resolved over 100 long-standing open problems in mathematics, including the Navier–Stokes Millennium Prize problem. The rapid progress of this model has surprised OpenAI's mathematicians, prompting internal discussions on how to best inform and prepare the broader community for these advancements. Melanie Matchett Wood from Harvard is involved in these discussions.
This report is the first public announcement from OpenAI about its new model, which has already resolved over 100 long-standing math problems, unlike previous AI advancements that focused on single problems.
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发布当时偏移：UTC+02026年9月21日 12:00 UTC
收录当时偏移：UTC+02026年9月21日 18:01 UTC
- 发布
- 2026年9月21日 12:00
- 收录
- 2026年9月21日 18:01
- 来源类型
- 官方发布
- 档位
- 当事方
- 信源状态
- 正常
档位是按信源手工设定的编辑判断，不是逐条打分。
讨论趋势
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On August 28, we began training a new internal model. In addition to resolving the Navier–Stokes Millennium Prize problem , this model has now resolved more than 100 long-standing open problems across most areas of mathematics. The pace of its progress in mathematics has surprised the mathematicians within OpenAI. This has led to internal discussions on the best way to inform the community of the rapid progress to prepare and adapt the field.
Mathematics is a fundamental science, and novel discoveries may result in a wide range of applications. That makes the promise of these capabilities substantial, and their responsible development and deployment important beyond mathematics itself. We recognize this and are working through how broader deployment of math-related AI capabilities can be done responsibly.
In a recent open letter “ A Severe Misalignment of AI in Mathematics (opens in a new window) ,” mathematicians raise concerns about the negative externalities of solving open problems as a benchmark for new AI systems.
Their criticisms highlight the need for thoughtful engagement of AI companies with the math community. To that end, we’re working with mathematicians who have established an independent mathematics advisory group. This group will serve as a bridge to the mathematical community and broader public, giving mathematicians a voice in how we move forward.
The group will advise on the review and communication of emerging results: they will help OpenAI assess their significance, advise on how to coordinate their dissemination, and advise on academic and professional standards of mathematical research. It will also advise on how our tools can support mathematical research and learning. We want to put capable tools in mathematicians’ hands so they can pursue the questions they know best and develop new ideas.
The group will operate independently from OpenAI. The group will have the freedom to offer advice we have not requested, comment on OpenAI’s impact on mathematics, and make its advice public. Its value depends on its members being able to exercise their own judgement and challenge ours. Its members will not be paid by OpenAI, and the group can change its membership as it sees fit. Importantly, the group will not be responsible for advising us on how to pace our internal progress on mathematics.
Working with this group is a first step. There are difficult questions ahead about how AI can support mathematical understanding and how the benefits of these capabilities can reach the wider community. We want mathematicians to be at the center of shaping the answers.
- François Charles (ENS-PSL)
- Camillo De Lellis (IAS, GSSI)
- Timothy Gowers (Collège de France, Cambridge)
- Martin Hairer (EPFL, Imperial College London)
- Nikhil Srivastava (Berkeley, Simons Institue)
- Ulrike Tillmann (Oxford, INI)
- Ravi Vakil (Stanford)
- Edward Witten (IAS)
- Melanie Matchett Wood (Harvard)