# AI in mathematics

A coalition of Caltech mathematicians—including the original Mathathon organizers, two coauthors of the Open Letter about the Mathathon, and other community members—issued a joint statement announcing that Mathathon is being redesigned around the theme "Old Problems, New Proofs": participants pick a solved problem with an unintuitive solution, learn as much as possible in 40 hours and present findings to peers, then take two months to develop an alternative proof or exposition, submitting an explainer in any form plus a GitHub repository holding whatever produced it (LLM chat histories, visualization code, and more); the event runs November 13–15, is co-organized with the Foundation for Science and AI Research (SAIR), has XTX Markets as lead donor, and no longer takes sponsorships from dev

A coalition of Caltech mathematicians—including the original Mathathon organizers, two coauthors of the Open Letter about the Mathathon, and other community members—issued a joint statement announcing that Mathathon is being redesigned around the theme "Old Problems, New Proofs": participants pick a solved problem with an unintuitive solution, learn as much as possible in 40 hours and present findings to peers, then take two months to develop an alternative proof or exposition, submitting an explainer in any form plus a GitHub repository holding whatever produced it (LLM chat histories, visualization code, and more); the event runs November 13–15, is co-organized with the Foundation for Science and AI Research (SAIR), has XTX Markets as lead donor, and no longer takes sponsorships from dev

Using OpenAI's September 8 announcement of a finite-time blow-up proof for the forced Navier-Stokes equation as an entry point, Tapio Schneider distinguishes episteme (explanatory understanding) from techne (the craft of prediction), and argues that when predictions must be trusted before they can be empirically verified—as in decadal climate projection or the design of a novel aircraft—trust comes from an auditable causal chain running from assumptions and input data to outcomes, each link of which can be tested individually; AI should therefore be embedded in auditable scaffolds such as physical conservation laws and used to learn closure models that can be checked against high-resolution simulations, observations, or experiments, rather than deployed as end-to-end models.

Using OpenAI's September 8 announcement of a finite-time blow-up proof for the forced Navier-Stokes equation as an entry point, Tapio Schneider distinguishes episteme (explanatory understanding) from techne (the craft of prediction), and argues that when predictions must be trusted before they can be empirically verified—as in decadal climate projection or the design of a novel aircraft—trust comes from an auditable causal chain running from assumptions and input data to outcomes, each link of which can be tested individually; AI should therefore be embedded in auditable scaffolds such as physical conservation laws and used to learn closure models that can be checked against high-resolution simulations, observations, or experiments, rather than deployed as end-to-end models.

This guest post by the editors of the communal blog Proofs and Prompts describes why the blog was started and what it aims to do: as AI became a central topic of conversation in the mathematical community, the editors created a collective space for mathematicians who lack a natural platform, especially PhD students, and reports that about a month after launch contributors ranged from PhD students and undergrads to Fields medallists and hobbyists, with posts ranging from a call for a general moratorium on AI to the view that AI plays too small a role in mathematics, while noting that contributors remain concentrated in Western Europe and North America, few have experience developing or evaluating LLMs, and only a handful of the forty-or-so published posts were written by women, and inviting

This guest post by the editors of the communal blog Proofs and Prompts describes why the blog was started and what it aims to do: as AI became a central topic of conversation in the mathematical community, the editors created a collective space for mathematicians who lack a natural platform, especially PhD students, and reports that about a month after launch contributors ranged from PhD students and undergrads to Fields medallists and hobbyists, with posts ranging from a call for a general moratorium on AI to the view that AI plays too small a role in mathematics, while noting that contributors remain concentrated in Western Europe and North America, few have experience developing or evaluating LLMs, and only a handful of the forty-or-so published posts were written by women, and inviting