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Navier-Stokes – Tristan Buckmaster [pdf]

cims.nyu.edu

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Re: Navier-Stokes – Tristan Buckmaster [pdf]

#511

Post by lhd1 »

defmacr0 wrote: post #210
lhd1 wrote: post #144

Earlier quoted context omitted.

I find the framing a little strange, a sort of David vs Goliath (with his enormous computational resources at his disposal). Since Levent is at Anthropic whose internal models are presumably as capable as anything OpenAI has. So why wasn't Anthropic behind their effort? Why did Tristan use OpenAI's models when it should have been known was a potential outcome? I understand they wanted a normal math collaboration but…

> So why wasn't Anthropic behind their effort? Presumably because this was something Levent did in his spare time and because it was not obvious that this work would eventually lead to a breakthrough. > Why did Tristan use OpenAI's models when it should have been known was a potential outcome? I'm sure in the past he had less cynical feelings about OpenAI and their penchant for academic fraud. > I understand they wan…

So what do you think his contribution was? His preprint record shows no research on fluids - and the statement says that the first LLM-generated proof Tristan received from Levent was 'the most horrendous I have ever read.' Levent is out for mathematical scalps whether it is in his field of expertise or not, and he has the resources to do it. And I am not saying he is not a very clever person, but the idea that you can bring yourself up to the forefront of research in PDEs, in particular NS, and contribute new ideas in less than a year is implausible.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#512

Post by dekhn »

phendrenad2 wrote: post #248

Big universities like Standford should be building their own AI datacenters. It's the only way to keep your research private.

LOL, have you worked for a big university? They are massively unsuited for building and running datacenters (especially warehouse-scale ones). Further, building an AI datacenter in California is daft.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#513

Post by alexjurkiewicz »

highfrequency wrote: post #412

From OpenAI: > While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models This is the crux of it. If Tristan's work and insights were not used to train OpenAI models, then this just looks like a case of hyper-competitive academic sniping that has been going on for decades (check out Watson and Crick!) accelerated by AI as a tool. The fact that this is…

> While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models

This is covering for Tristan saying something like, "Actually, I was using my friend's account for half of this work".

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#514

Post by dekhn »

epsteingpt wrote: post #37

Only here to say, regardless of the drama, shouldn't we all be excited if the Navier-Stokes gap is closed? Time will almost certainly reveal a lot more about the drama and the related ethics, but let's get excited about the actual breakthrough as well!

I would be excited if somebody could use this to show an unexpected or interesting behavior in the real world.

Tao mentioned "finding a configuration of water molecules that would collapse and shoot off to infinity", which would qualify IMHO.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#515

Post by Catloafdev »

vlovich123 wrote: post #497
Catloafdev wrote: post #425

Earlier quoted context omitted.

These responses seem to me to make it abundantly clear who's telling the truth here. I wonder who this fools. It would be extraordinarily easy to simply say, this model was not trained on your work, if that were the case. It's telling that they refuse to acknowledge the root issue here, and are attempting to shift the conversation elsewhere.

I'm not sure it's so easy to tell whether a given piece of data was in a training run at their scale. It's entirely possible they think the answer is no, but on the off-chance that it could be, they'd rather not say no and then later it turns out they did and then they're claimed to be lying. If you were them, unless you could 100% rule it out, you'd hedge and say you can't.

It may not be easy, quick, or simple to figure that out - absolutely fair.

But it is knowable. Their entire business is built around training models - they have the ability to know exactly what was in any given training run.

I guess time will tell.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#516

Post by dekhn »

wwind123 wrote: post #97

Hard mathematics problems used to take years if not decades to tackle manually. But now with enough compute and a hint that a certain approach might work, it just takes a few days. This could be the last year that humans could still make more substantial contribution to major match problems than machines.

Um.... good! Mission accomplished.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#517

Post by anonymousDan »

fc417fc802 wrote: post #489
myrmidon wrote: post #447

Earlier quoted context omitted.

Just replace the model with a human student. "Training" on textbooks => fine "Training" with unpublished notes from another professor, then publishing something on that exact topic with a similar approach without giving any credit => extremely questionable.

Presumably the professor voluntarily provided the notes in this analogy. I think the student would also be expected to cite the textbook if building off of it directly. In contrast, humans are generally not expected to cite "general inspiration" or what have you. So if we're to apply human standards, and assuming that the model was trained on the relevant work, it would only be plagiarism if the model directly built…

This is just a nonsense line of reasoning. Training based on the solution to the problem (or the key insight behind the problem) is clearly a form of plagiarism.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#518

Post by tgma »

zeven7 wrote: post #467
CoolestBeans wrote: post #460

Earlier quoted context omitted.

I think this might be a red herring. All it takes is someone to get an inkling that someone is working on a new approach and seeing some success for OpenAI to fire the AI cannon at the problem. The community seems fairly small (from this outsider's point of view). The idea that the data made it into the training set and that's how the bot figured it out is definitely possible, but I would want to rule out the simpler…

Terence Tao said the same[1] > In fact, it is now the identification of a promising problem which is the scarce and precious resource. We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any…

> it is now the identification of a promising problem which is the scarce and precious resource

This is by no means new. Perhaps it is even more extreme now. Literally my first 1:1 with my PhD adviser back then, he told me that the most important thing about a researcher is the quality of the problems he picks.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#519

Post by mahogany »

simonw wrote: post #461
dgellow wrote: post #434

Earlier quoted context omitted.

And that’s bad, right?

It's legal. I wouldn't do that myself, but I guess that's why I don't train models for a frontier AI lab.

The thread is not really about what's legal; the topic is integrity. It sounds like, based on the fact that you wouldn't do it yourself, you agree that it's not a good thing to do.

Re: Navier-Stokes – Tristan Buckmaster [pdf]

#520

Post by highfrequency »

bigfish24 wrote: post #432
highfrequency wrote: post #412

From OpenAI: > While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models This is the crux of it. If Tristan's work and insights were not used to train OpenAI models, then this just looks like a case of hyper-competitive academic sniping that has been going on for decades (check out Watson and Crick!) accelerated by AI as a tool. The fact that this is…

Opt out doesn’t guarantee they can’t train on “your” data. Legally the reasoning tokens are ambiguous in terms of ownership. Explained this here https://fortune.com/2026/08/26/alex-karp-was-right-you-dont-...

This would be a fairly insane breach of trust and common sense if true; the chain-of-thought / reasoning trace is, from an information perspective, close to a superset of the prompt and model response.
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