[data_maan]

Very serious for mathematicians - not for ML researchers.

If the paper would not have had the AI spin, would those 10 questions still have been interesting?

It seems to me that we have here a paper that is solely interesting because of the AI spin -- while at the same time this AI spin is really poorly executed from the point of AI research, where this should be a blog post at most, not an arXiv preprint.

[_alternator_]

I’m confused by this comment. I’m pretty sure that someone at all the bigs labs is running these questions through their models and will report back as soon as the results arrive (if not sooner, assuming they can somehow verify the answers).

The fact that you find it odd that this landed on arXiv is maybe a cultural thing… mathematicians kinda reflexively throw work up there that they think should be taken seriously. I doubt that they intend to publish it in a peer reviewed journal.

[data_maan]

Yes, but people at those labs may be running those problems because a Fields Medalist is in the paper, and it got hype.

Not because of the problems, and not because this is new methodology.

And once the labs report back, what do we know that we didn't know before? We already know, as humans, the answer to the problems, so that is not it. We already know that LLMs can solve some hard problems, and fail in easy problems, so that is not it either.

So what do we really learn?

[heliumtera]

the last unsolved erdos problem proof generated by llms that hit the news was so non interesting that a paper published by erdos himself stated the proof

aaaaaaand no one cared enough to check

so i think the question is, are those interesting by themselves, or, are they just non interesting problems no one will ever care about except it would be indicative llms are good for solving complex novel problems that do not exists in their training set?

[j_maffe]

The timed-reveal aspect is also interesting.

[data_maan]

How is that interesting for a scientific point of view? This seems more like a social experiment dressed as science.

Science should be about reproducibility, and almost nothing here is reproducible.