“At the moment, the mathematics community is facing an unprecedented misalignment brought on by the advent of LLMs. According to the standard telling, the basic tension is between our traditional values (whatever those may be) and those of the corporations that make and market LLMs. […]”
In this personal essay, I reconstruct the reasons why I chose to become a professional mathematician. In particular, I want to find out whether I would still find these reasons convincing and motivating today, at a time when AI is becoming capable of conducting excellent and potentially superhuman mathematical research.
To grieve, or not to grieve? (Mathematics and AI). ~ Kevin Buzzard. https://xenaproject.wordpress.com/2026/10/01/to-grieve-or-not-to-grieve/ #AI4Math
In response to an exponential growth of submissions, the arXiv just announced changes to its rate limiting policy, which among other things restricts submissions to at most two a month for each submitter.
Math is the one domain where you can actually verify AI output mechanically — a proof checker doesn't care how confident the model sounded. Smart wedge: formal verification gives you ground truth for free.
What did the Lean proof of Fermat's Last Theorem formalize? ~ Justin Asher. https://justinasher.me/what-did-the-lean-proof-of-flt-formalize #LeanProver #ITP #AI4Math
About the possibility of new paper mills in mathematics.
Preprints are central to math research and immediately feed back into LLMs. Could the mainstream preprint workflow be adjusted in a way that gives individuals more choices to impact the balance between creation of novel papers and digestion of knowledge?
definitely going to be new AI-enhanced areas which are totally underserved now, and will be high value.
but even here I think jevons strikes: formal verification gets so much cheaper, that people want it everywhere!
because few people can do it (employment already ⬇️): salary ⬆️
If you love mathematics as much as I do, and despise AI as much as I do, then you may want to read this. Kudos to @glynmoody for putting this together. https://www.techdirt.com/2026/09/30/in-the-wake-of-the-latest-unprecedented-ai-proofs-what-now-for-mathematics-and-mathematicians/ #Math #Maths #AI
The Nine Wise Mathematicians have handed down their recommendations for AI companies who want to prove big mathematical theorems. Their first recommendation is "Don't." It kinda weakens their point that they don't stop there. agmai.org/general-sep29/
As an editor, I'll put it plainly:
These are the structural forces that are shaping peer review right now. It is a lousy time
The EMS should establish a structure to shepherd the use of artificial intelligence in mathematics research. This could include developing in house AI models, regulating their use by the mathematical community in accordance with European law and establishing protocols for publication.
A brief post to note two reports that just came out: The Advisory Group on Mathematics and Artificial Intelligence (AGMAI) has posted its general recommendations to AI companies with regards to generation of solutions to mathematical problems. A working group report “AI and TCS: The Next Six Months” arising from a two-day event at the […]
“As more and more central problems are nuked into oblivion by generated proofs, against the spreading AI-gloom the voices rise, emphasizing the internal value of deep mathematical thought and advocating for measures ranging from exercising extreme discretion with generative tools to seizing railway stations and the telegraph (or was it departments and classes of mathematical problems?) from the ti…
My broad answer to that is that if they don't follow some of our recommendations, it will be publicly checkable that they have not done so. Maybe that's not a "plan" in the sense you are talking about, but I hope it's at least something.
[This is a guest post by Rachel Webb. This blog post was initially written in a different file format and converted using AI. — T.] The way I do math experienced a great upheaval once before, halfway through my PhD. I was in a seminar with a group of graduate students and faculty developing new […]
We took carefully into account feedback from over 600 mathematicians. Of course, they expressed a range of views, but I think that on each specific issue the majority view (of those who expressed a view) was well aligned with the prevailing view on the committee. 3/3
One of our main recommendations is that such results should be posted as quickly as possible, where that means as soon as certain minimum standards have been met, as outlined in particular in Section 2 of the recommendations. 2/3
The advisory group on mathematics and artificial intelligence, of which I am a member, has just published a set of recommendations concerning the responsible release of mathematical results generated by AI companies using internal models. 1/3
agmai.org/general-sep29/
Mostly not posting right now, But, I've just read that a sieve approach (similar to my result from 2023) is being used to prove the twin prime. I'll probably never get credit for all that. What a terrible time to be Math_Different. Still, kudos to the woman solving the problem (with ai, presumably using my proposed proof and several others like it). Is THIS what my targeting is about, stuff like T…
The wider space of anomalies to the Navier-Stokes equation is a far better and more interesting approach to looking at the Clay Mathematics Institute's original set of four problems than the OpenAI attempt at shortcutting the Millennium Prize itself to the detriment of the mathematical community.
Stepan Nesterov, PhD student at Stanford University About a hundred years ago, David Hilbert gave a rigorous definition of a mathematical proof: Definition. A mathematical proof is a sequence of formal statements A1,…,AnA_1, \ldots, A_n such that every statement AiA_i in the sequence either belongs to a predefined set of axioms of logic or there […]
We might look to the large AI companies, but I don't see why they would continue to fund pure mathematics research once the initial glow of intellectual prestige it generates starts to tarnish. Diminishing returns. The public and media will grow bored of talking about Millennium Prizes and whatnot.
#OpenAI says its agents solved a Millennium Prize Problem in 88 hours but top mathematicians are furious because a proof nobody understands is a missed lesson in most of mathematics and a serious danger in the one field that protects our encryption. lnkd.in/p/eRDS4Zbc
Would people on Bsky saying in unison "An LLM solved a Millennium Prize math problem" change anything about OpenAI hacking companies/not being accountable for them? No.
"Learning to Discover Interesting Mathematics" We define intrinsic interestingness of a theorem as the ratio between the length of its proof and the length of its statement. They then trained a 27B model to predict proof difficulty and used it to grow a self-expanding library of interesting results. It's a crisp attempt at the question AI math will soon face: which of the millions of provable stat…
[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, […]
there's basically unanimous agreement among mathematicians that the Navier-Stokes Millennium Prize problem is now solved, among many other previously unsolved problems in other areas of mathematics.
OpenAI addresses AI safety with new collaborations.
OpenAI is collaborating with an advisory group of mathematicians after its model reportedly solved the Navier–Stokes problem. Despite progress, OpenAI stresses autonomous recursive self-improvement shouldn't be rushed…
Read more on Kimbodo:
A coalition of Caltech Mathematicians. 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 Open Letter about the Mathathon, and other members of the Caltech community engaged in a conversation to redesign this event. We quickly […]
The Glimmer in our Eye (with Grant Sanderson) - Numberphile Podcast AI has become the biggest talking point in mathematics. We spoke with Grant Sanderson (creator of the 3Blue1Brown YouTube channel). https://www.youtube.com/watch?v=CBYhaKgah5U #GrantSanderson #Numberphile #BradyHaran #Maths #math #AI #3Blue1Brown
I imagine it's referring to this: en.wikipedia.org/wiki/Navier%...
It's claimed that OpenAI used the inputs from Buckmaster & Alpoges (who were paying for Claude) to improve their models which had previously failed to produce results.
Now that we have proof that AI generated findings are more likely to be research misconduct taken from those who uploaded their findings, perhaps institutions should rethink AI contracts? If it doesn't prohibit use of what you upload to train the model, you're agreeing to the misconduct.
Why does AI worry applied mathematicians less? We have met the machine before: human computers moved up to designing algorithms, and a computer run led to the solution. Driven by problems rather than great conjectures, we expect AI, used with careful verification and validation, to make us better mathematicians.
This essay was, in some sense, written as a way to grapple with the questions raised by AI about my own value as a researcher. It is a reflection on the effects these changes were having on me, on my practice of mathematics, and on how I might learn to cope with them.
Dear Scientists:
Please don’t do this. Giving years of your hard work to the plagiarism machine is a very dumb thing to do.
See also: The mathematical proof “discovered” after some mathematicians gave the plagiarism machine a bunch of THEIR hard work.
Same thing happened with a recent soln for a famous math proof...
[This is a guest post by Jess Werk. This blog post was initially written in a different file format and converted using AI. — T.] Hello, I am Jess Werk, professor and chair of Astronomy at the University of Washington. Astronomy may interest mathematicians right now because our field has already been reshaped by supercomputers […]
AI feels like the double edged gift of mathematics. It can unlock new ideas and reveal patterns I would not have found on my own, yet it also disrupts our traditional proofs and calls into question how we truly understand what we are doing. I want to stay grounded in rigor even as I marvel at the promise and the potential. #AI #math #future