One predictable (but dispiriting) trend in the academic response to math x AI is the consolidation of power. Meetings like this cmsa.fas.harvard.edu/aimathp… involving closed circles of (American) mathematical ``leaders" who aim to set guidelines for others. (1/1)

I hope this will inspire more mathematicians to contribute to a deeper theoretical understanding of ML, which seems so sorely needed for the technical side of AI safety!

We are seeing a new trend in submissions to @arxiv (and presumably to conferences and journals): Authors submitting papers whose contents they likely do not understand. 1/

I had a long conversation with a journalist today (for a piece that may show up on Sunday) and he asked me about something that many here also misunderstand: "Why wouldn't mathematicians want solutions to famous open problems if they are found by or with the aid of AI/LLM's??" And the answer is that **we absolutely want to know the solutions** to the riddles that have "haunted" math for so long! But not *at any cost*. Not at the cost of the future of our profession. Not in a hurry just to make headlines before an IPO. It is undeniable that the LLM's are clearly amazing tools and they can be harnessed to aid and accelerate mathematical discoveries. And I fully expect these tools are here to stay, and they will be incredibly helpful in some arenas. But as many others have already said, math is built by humans for humans and it's built on *understanding* and *absorbing* (slowly, because it takes humans a long time to distill the essence of a new proof, the main ideas) of the ideas and techniques that we invent and discover. When a huge result is proved by an LLM and a 100+ page paper is produced, and no one associated with that particular company/paper can actually explain what are the key new mathematical ideas that have made the proof possible, then there is very little to gain from that paper/result. At least until a human mathematician takes the time to read it, digest it, and distill the key ingredients so that the rest of us can digest it and hopefully apply it to other settings. Such a paper is the equivalent of the pop-culture answer "42" to the ultimate question of life, the universe, and everything. But what if we get a 100+ proof every week, that no one understands and the LLM companies make little effort to make understandable? How do we process all of these "would be" advancements? I say would-be because they are not advancements until the *community* is able to advance with the new knowledge! The thing is that there is a viable alternative: the LLM companies give up their arms race (which is in essence adversarial not just to each other but also to the math community) and actually become scientific partners of the community. How so? Suppose OpenAI wanted to prove Navier-Stokes and get credit for it (which of course they did want). Then six months ago they could have invited the top 10 fluid dynamics researchers (including Córdoba, Zoroa, etc) to form a team to solve it using their products. OpenAI gives funding to each faculty member for a course release, and they fund a number of grad students for the semester to work on this too (and be trained in LLM-aided research in the process). OpenAI provides resources and the community provides expertise. Eventually they prove (or make progress) on this important problem and there are several positive outcomes: 1) OpenAI gets credit for propelling scientific progress, and their tool is solidified as a terrific tool for team work on advanced research. 2) The researchers understand how the proof was built, and they are able to write a human-readable paper that peers can use to advance knowledge. They can give talks about this work and help the community digest the new insights, and advance the field. 3) The experts know the literature and they can identify when an LLM is using a piece that is due to a certain person. They can identify the correct references and *give deserved credit* to the work being used. Or even better, invite those researchers to join the project at that point, since their work is heavily used by the LLM. In other words: stop or try to minimize scientific plagiarism and dishonest practices. 4) Grad students get to learn material, new techniques, understand the capabilities of what LLM's can and can't do, and participate in a new model of research collaboration (plus they get funding).

Linked paper is: Alexander Borisov, Ofer Gabber, Adrian Vasiu, “On endomorphisms of affine spaces and the Jacobian problem” arxiv.org/abs/2609.05746 — Read the “Supplemental AI statement of the third author [Vasiu]” on page 160.

Anabelian geometry in Yokohama 2026 Let's the mathematics flow 🎊

Clay Institute won't call Navier-Stokes solved after OpenAI proof claim. ~ Marcus Schuler. implicator.ai/clay-institute… #AI4Math