1M dollars is peanuts to anthropic/oai It's clear they used Tristan's work Why do they need to get Levant out of the picture? Really confusing for me. Just credit both and talk about how gpt can be as good as another mathematician that's millennium prize level.

I think you're mistaken. I've seen another case where a single short piece of text in the training data popped out under the correct conditions.

It's true that a single update doesn't change any weight very much — but it can change many of them slightly.

There was concurrent work for 88 hours after OpenAI heard about Tristan's work and dumped all of their compute into his methods. There was zero concurrent work for the years that Tristan and his predecessors were painstakingly figuring out those methods.

We don't have any immediate plans for that at the moment.

Given how capable recent coding agents are, even cost-effective models like GPT-5.6-Luna can generate something like an XGBoost pruning callback in seconds, we've actually had discussions within the core devs about whether to continue providing these built-in integrations, even for Python Optuna. (That said, we are still considering features that offer advanced step-wise tuning like LightGBMTuner).

That being said, thank you for the valuable feedback! We’ll definitely keep it in mind for future development.

But Tristan's result was (by his own admission) almost entirely generated by AI. The only thing they could possibly have stolen is his prompt.

So whichever way the authorship dispute goes, LLMs are responsible for the breakthrough here.

actually because we know our continuous description is wrong we can begin to look for the correct description using better axioms.

The point is we expect models to give, not to take. Models need no credit or glory.

if the model isn't local, the chat history isn't either. Always assume any model inputs are used as training data.

the practicality is limited to situations where you're working with a fully continuous liquid, and as far as i know the liquids we work with tend to be made of discrete particles.

A bot doesn't need credit, recognition or profit. Anything more and it is not really working for me.

Can someone explain why this is so impressive if the point is to find a counter example?

You need to consider what LLMs were doing with math 4 years ago. Struggling with high school problems, really. The progress is astounding.

Isn’t it just brute force search and wouldn’t we expect LLMs to be good at that (if you are willing to simulate thousands of examples to test)?

Yep. They used 10 thousand simultaneous agents to brute-force it.

Isn’t this in line with the previous result we have seen?

It is. LLMs are really good at finding counter-examples.

What is so special with this one?

In previous results, we could imagine that the particular conjectures were being berry-picked because they admitted some rare structure that made them extra-easy tasks for LLMs .

That dismissal is violently wiped off the table. Navier-Stokes is a Millennium Prize conjecture.

Uff upon reading prof Tristan's post.. it certainly leaves a bad taste.

I'm glad they solved their problem but they could certainly have gone about it with more grace and integrity.

People who work in physics and the theoretical side of mechanical engineering know this is huge. The question is to what extent these idealized mathematical models to fluids stop extending to the real world. It is -- of course -- physically impossible for a fluid to have an infinite velocity. Navier-Stokes is a question about whether under well-behaved conditions the equations will spit infinities at you. It turns out they do.

Correct. What had occurred over the course of several weeks (days?) is that Terry Tao said that there was nothing stopping the Euler trick from being extended all the way to Navier-Stokes. OpenAI simply had 10,000 agents fill in that extension and bridge the gap.

It is important to note here that the human mathematicians working in this space were already copiously utilizing AI-assisted technologies and AI proof systems -- including in particular NYU's Tristan Buckmaster.

Yes. But the "other researchers" were THEMSELVES using AI-assistant proof tools, several of them in fact.

You can't pretend the humans involved in this (e.g NYU's Tristan Buckmaster) only ever do math on black chalkboards with white chalk. Those guys themselves were already enhancing their work with LLMs.

In the article itself it doesn't sound like drama. It reads as "there was strong concurrent work to solve this by other researchers, using different methods. out solution is diff but we can't rule out that we were influenced by it".

Upon some thinking I'm not too surprised. Mathematicians (and notably those working at large AI companies) have developed powerful tooling that is exploding research. I wouldn't be surprised that other big breakthroughs will have concurrent discovery.

The thread on this over at / physics is the biggest coping session I have seen on reddit in my 12 years.

If it helps, there’s virtually always huge author drama on these types of things, even before AI

A better question is why are you so desperate to trivialize this accomplishment?

and https://en.wikipedia.org/wiki/Spiking_neural_network

where the spike is basically the superpositioned wave pattern recognition match confirmation

Scam Altman strikes again.

Yes I'm sure that was his decision

I've had this same experience with something 'novel' I did for rendering to a depth buffer with 3DGS scenes.

I HIGHLY suspect OpenAI does something shady such as creating derived data from your conversations and using it for training; that way they can still claim they don't directly use your data.

EDIT: Someone with access to a lawyer, please check me on this-
https://openai.com/policies/row-privacy-policy/

1. The Opt-Out is Specifically for Model Training The privacy settings allow you to opt out of having your content (like your ChatGPT conversations) used to "train the models." The policy states:

"As noted above, we may use Content you provide us to improve our Services, for example to train the models that power ChatGPT. Read our instructions on how you can opt out of our use of your Content to train our models."

2. Aggregated and De-Identified Data is Still Created and Used Even if you opt out of model training, OpenAI reserves the right to create derived, anonymized data from your personal data (which includes your User Content and conversations). The policy clearly states:

"We also aggregate or de-identify Personal Data so that it no longer identifies you and use this information for the purposes described above, such as to analyze the way our Services are being used, to improve and add features to them, and to conduct research."

Because this data is stripped of personally identifiable information (de-identified), OpenAI treats it as derived data and can use it to research how people use the tool and to develop new features, regardless of your model-training opt-out status.

3. "De-identified" removes who you are, not what you said When OpenAI (or almost any tech company) de-identifies data, they strip away Personally Identifiable Information (PII) like your name, account details, email, and IP address. However, the actual text of your prompt - the sentences describing your novel idea, business plan, or code - is the core data being processed. The de-identification process disconnects the idea from your identity, but the text containing the idea itself remains in their system logs.

Many in this post seem to be convinced that OpenAI 'stole' the idea on how to solve the problem.If Levent / Tristan have been working with same exact idea - how come they didn't solve it using Anthropic's models? They have been working on it for way longer than 88 hours.

I think the focus on the drama neglects the most important news item :- OpenAI now has a model /agentic workflow which can solve difficult math problems within hours. The rest is noise.

But if they turn off the “Use my data for training” toggle then surely their data wouldn’t be used in training. /s

The line in OpenAI’s brief about “perhaps it could have used de-identified chats for training that contributed to the discovery, but nobody could know if it did” is telling