I think the answer to this is simple: Mathematicians don’t own math. Math is a tool to do useful things. I don’t want this gatekept by people who have a sense of entitlement. And luckily enough, they don’t have any leverage, so the appropriate response is to say no.
Math is not merely a tool. The question isn't one of doing the math, it's about publication and release of work. Humans have to follow certain rules, so why should we allow AI labs to behave differently?
Care to explain? If a paper is not interesting, it would not get past reviewers. Unless "interesting" is not part of the criteria for entry to the journal? That sounds interesting if true, but that certainly has never been my experience.
I feel like I am going crazy, since this is taught in most academic writing courses. Being replicable or persuasive or correct is not enough. It needs to be perceived as valuable, or it will not get through. I learned that the hard way, and found others that taught this message only later.
Publish whatever you want vs publish in a particular journal.
You want to publish in a journal you follow their rules. You want to publish something? Go ahead.
And results are not great results because they are published in top journals. Top journals are top journals because they attract the publication of great results. But the math always speaks for itself.
This is a fantasy I remember hearing in grad school. Get a little more senior and the whole perception shatters. You can put whatever rubbish you want out there. But if you are getting attention, or want attention, you have to play by the rules. Those rules are whatever the community dictates. Otherwise, why would Nick Polson be at risk of being discredited? It's no different from any other field, math is not special. Correctness is a necessity. It is not sufficient.
It's not that murky anymore. It's been determined none of the chat contents were used. If you don't believe OpenAI, that's your choice.
See <a href="https://openai.com/index/navier-stokes-solution/" rel="nofollow">https://openai.com/index/navier-stokes-solution/
> Following an investigation, we have confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training. The OpenAI internal model used for this result was developed through large-scale reinforcement learning on top of a previously pretrained model. Our proofs also differ significantly. In the Euler case, Alpöge and Buckmaster proved a result with external forcing, while OpenAI’s system proved a result without external forcing.
At this point it takes some weird epistemics to think they'd need to outright fabricate the outcome of an investigation when countless other mathematical breakthroughs have been solved the same.
They are certainly biased, but I don't think such bias is strong enough to cause them to lie about facts. We may agree to disagree here.
Absolutely not. Otherwise, if I prove something original, and it is correct and replicable, this would mean I can publish in Annals of Mathematics. Now if we narrow that to publishing somewhere, then sure, but that's irrelevant. This is about values for prominent results (e.g. Annals) for mathematicians.
This was my point though. Not sure why you are disagreeing? The original argument was that there was no criteria for publication other than replication, or that is how I read it. I was refuting that.
Perelman was already working on an interesting problem, and wrote in ways that were less formal, but still academically sound, so of course he did not need to publish it. What about the people working on problems that are not interesting? You don't think they encounter significant difficulties getting their work out there?
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You have a very institutionalised idea of what publishing means.
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I feel like I am going crazy, since this is taught in most academic writing courses. Being replicable or persuasive or correct is not enough. It needs to be perceived as valuable, or it will not get through. I learned that the hard way, and found others that taught this message only later.
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You want to publish in a journal you follow their rules. You want to publish something? Go ahead.
And results are not great results because they are published in top journals. Top journals are top journals because they attract the publication of great results. But the math always speaks for itself.
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See <a href="https://openai.com/index/navier-stokes-solution/" rel="nofollow">https://openai.com/index/navier-stokes-solution/
> Following an investigation, we have confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training. The OpenAI internal model used for this result was developed through large-scale reinforcement learning on top of a previously pretrained model. Our proofs also differ significantly. In the Euler case, Alpöge and Buckmaster proved a result with external forcing, while OpenAI’s system proved a result without external forcing.
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...and, I would add, it would be an informed choice, wouldn't you agree?
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They are certainly biased, but I don't think such bias is strong enough to cause them to lie about facts. We may agree to disagree here.
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Perelman had no publications in annals and didn’t even publish in English or provide every possible proof yet he blew open two fields.
Your claim is does not stand to even the barest scrutiny
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Perelman was already working on an interesting problem, and wrote in ways that were less formal, but still academically sound, so of course he did not need to publish it. What about the people working on problems that are not interesting? You don't think they encounter significant difficulties getting their work out there?
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