Is it currently possible to reliably limit the cut-off knowledge of an LLM (either during training or inference)? An interesting experiment would be to feed an LLM mathematical knowledge only up to the year of proving a theorem, and then see if it can actually come up with the novel techniques used in the proof. For example, having only access to papers prior to 1993, can an LLM come up with Wiles' proof of FLT?

Why have they still not released a paper aside from a press release? I have to admit I still don't know how auspicious it is that running google hardware for three days apiece was able to find half-page long solutions, given that the promise has always been to solve the Riemann hypothesis with the click of a button. But of course I do recognize that it's a big achievement relative to previous work in automatic theorem proving.

Anyone else feel like mathematics is sort of the endgame? I.e., once ML can do it better than humans, that’s basically it?

Mathematicians have been using computers, programming languages, and proof engines for over half a century; however breakthroughs in mathematics are still made by humans in any meaningful sense, even though the tools they use and make are increasingly complex.

But as things look now, I will be willing to bet that the next major breakthrough in maths will be touted as being AI/LLMs and coming out of one of the big US tech companies rather than some German university.

Why? Simply, the money is much bigger. Such an event would pop the market value of the company involved by a hundred billion - plenty of incentive right there to paint whatever as AI and hire whoever.

I think the interface of LLM with formalized languages is really the future. Because here you can formally verify every statement and deal with hallucinations.