Joint Statement about Mathathon

[This is a guest post by a coalition of Caltech mathematicians: the original organizers of Mathathon (Alvan Arulandu, Andrea Li, Avni Garg, Brian Zhao, Caiman Moreno-Earle, Sathvik Redrouthu), two coauthors of the Open Letter about the Mathathon (Dylan King, Tasmin Chu), and other members of the Caltech community (Mayla Ward, Merrick Hua, Robert Joseph George, Sergei Gukov, Shaowu Zhang, Tony Yue Yu, Vihaan Dheer). This blog post was initially written in a different file format and converted using AI. It is also crossposted at Proofs and Prompts— T.]

Mathathon is centered around one question: how can we use AI to augment human understanding of mathematics?

This past week, the original organizers of Mathathon, two coauthors of an open letter about the mathathon, and other members of the Caltech community engaged in a conversation to redesign this event. We quickly reached a consensus: problem solving is but one component of mathematics; mathematical understanding and exposition are similarly meaningful. But these components have often been overlooked by grantmakers and hiring committees. We want to celebrate human mathematicians who undertake this work.

Thus the new theme of Mathathon is Old Problems, New Proofs.

Consider the four color theorem, the ABC conjecture, or the Navier-Stokes problem. Many mathematicians find their proofs—or claimed proofs—difficult to understand or unsatisfying. We invite our participants to pick a problem with an unintuitive solution, learn as much as possible in 40 hours, and present their findings to their peers. Then, they will take two months to develop an alternative proof or exposition. Afterward, they’ll submit an explainer and a GitHub repository. The explainer can take any form: a paper, a blog post, a video, an interactive game, et cetera. The repository will store whatever was used to produce the explainer—LLM chat histories, code used to produce visualizations, and more—so the mathematics community can examine the ideas behind the finished product.

By shifting the focus from open problems, we want to encourage Mathathon participants to explore everything else we do to make sense of mathematics: generalizations, new notations and techniques, connections to other problems. These innovations can be more exciting than solving individual problems.

Mathathon treats AI as one of many tools available to mathematicians. We no longer receive sponsorships from developers of proprietary AI models. Participants are given a cash grant to purchase any tool they need, whether that be LLM credits, HPC access, or pen and paper. Proprietary models are allowed, but we encourage the use of open-source tools. Our goal is to empower each participant to make their own decision about the tools used at Mathathon and in their own work.

The new event will take place on November 13–15. It is co-organized with the Foundation for Science and AI Research (SAIR), which will provide compute, host workshops, and offer prizes to teams that use open-source models. SAIR is also administering travel grants for participants. Our lead donor is XTX Markets, an algorithmic trading firm and a major supporter of academic research, open-source infrastructure, and community-led initiatives in AI-for-math.

The organizers and advisors of Mathathon range from AI proponents to critics, from undergraduates to well-established mathematicians. Working with this group has been a learning experience for everyone involved. We hope that it will bring together a similarly diverse set of judges and participants.

If you want to deepen your understanding of math, we hope to see you at Mathathon. Our mission is for you to learn from and teach your peers.

If you want to have fun, we hope to see you at Mathathon. It’s still a student-led event, run with the same playfulness that first inspired it.

If you are curious about AI capabilities, we hope to see you at Mathathon. Finding alternative proofs to solved problems and presenting them in human-oriented ways is a novel challenge for LLMs.

If you are concerned about AI’s impact on mathematics, we hope to see you at Mathathon. It celebrates human-oriented expositions. Mathathon requires participants to fully disclose and justify LLM usage and asks them to consider whether smaller-scale open-source tools would suffice.

Finally, Mathathon isn’t a conclusive guide to AI in mathematics. The organizers hold different opinions about AI-assisted problem solving, open-source versus proprietary models, and more. But we all believe that mathematicians can use AI to advance human understanding. Mathathon is a first experiment. We want to pave the way for future initiatives that explore AI’s roles in learning, teaching, review, and research, while placing human understanding and community building at the forefront. Mathematics is undergoing its biggest change in decades, and we must work together to adapt.

Acknowledgements. We are grateful to Terence Tao and Andrew Wu for reviewing this statement and leaving comments, as well as to countless others who offered feedback about Mathathon. All of the views above are solely our own and do not reflect those of Caltech or any other organizations we belong to.