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PaperclipPerfector  2d ago (text post)   7561 thread views

Culture War Roundup for the week of September 7, 2026

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No posts about the AI plagiarism in mathematics?

Well, here you go

Tristan Buckmaster published a letter about some stuff in math, blah blah who cares.

The important thing is that he's essentially accusing OpenAI of spying on him and stealing his work in order to publish first. When questioned about this, OpenAI started backtracking and trying to get Buckmaster's coauthor, an Anthropic employee, removed as an author. Then, they threatened him

I asked when the first prompt had been sent by them. This question was not answered directly by OpenAI for some time. Eventually it was agreed that it had been sent in the past few days, after information about our work had reached OpenAI. I asked whether the model had been trained on, or had access to, our sessions in Codex, into which we had been putting all our drafts for the whole of this project. I was told the model did not look up user data. I asked again, about training, and I did not get an answer. Two proposals were offered to me. The first was that we post our Euler result, and that OpenAI post its Navier-Stokes result the next day. The second was that, after posting Euler, I alone write a paper presenting the Navier-Stokes result, acknowledging that an internal OpenAI model had resolved it. Sebastien twice asserted that he wanted Levent removed from authorship, and said it would all be simple if only it were not the case that, and it was so annoying that, Levent works at Anthropic. It was also said that if OpenAI posted after us, they would say that we deserved the Clay Prize, and that we were the “closest humans to the problem”. I declined both offers.

I said that if OpenAI released its result in the way proposed I would go public with what happened. The reply was, “Why would you ruin your career?” I replied that I am an academic, and asked why he thought going public would ruin my career. The reply was, “If you don’t want me to be nice, then I don’t have to be nice.”

I'm reminded of the fun song, Lobachevsky.

And when his work is done
Ha-ha! - begins the fun
From Dnepropetrovsk to Petropavlovsk
By way of Iliysk and Novorossiysk
To Alexandrovsk to Akmolinsk
To Tomsk to Omsk to Pinsk to Minsk
To me the news will run
Yes, to me the news will run!
And then I write
By morning, night
And afternoon
And pretty soon
My name in Dnepropetrovsk is cursed
When he finds out I publish first!

He is, of course, NOT accusing OpenAI of this. I am accusing Tristan of accusing OpenAI, Tristan is much more circumspect. But c'mon, if it stinks like shit maybe check your shoe. This looks like OpenAI stepped in something.

Sebastien Bubeck, responding for OpenAI, and himself, in non-specific terms.

More here with more background on the timeline and people involved.

The more important underlying issue is: frontier models are outcompeting aspiring academics in theoretical math, making open problems trivial, making them feel obsolete.

I think there's a workaround that lets academics (including Buckmaster et al) keep their pride and purpose.

Terrace Tao wrote a few days ago: AI largely autonomously solving open problems is bad, because the solution itself isn't very important, but the techniques (advances, insights, etc.) developed to find the solution and failed attempts are. These techniques assist future problems including ones with practical applications. Meanwhile LLM solutions are barely readable so we don't get any techniques from them, only the open problem solution.

So even if a model solves an open problem, it still makes sense for academics to do themselves, or at least break the model's solution into something comprehensible by humans, and receive credit for that. Buckmaster et al were already doing that:

I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable

So why don't they just finish their readable proof, publish, and get credit for that? Look, even if OpenAI hesitated to publish their allegedly stolen result, they still wouldn't be first, because another independent group has now also reached the solution. It's unfortunate that sometimes mathematicians reach the same discovery independently in parallel, but these ones still have a chance to be famous in a small group, just not in a (slightly larger?) small group.

another independent group has now also reached the solution.

This isn't the solution, though, is it? The only difference between incompressible Euler and incompressible Navier-Stokes is that the former has no diffusive term and the latter does, so I'm certainly not going to suggest Tao was wrong that "There does not seem to be anything in principle preventing the methods from extending all the way to Navier-Stokes", but that diffusive term is pretty important in this specific case if they weren't able to extend their results to Navier-Stokes immediately ... and it's pretty important in this context in general. These are conservative equations where free energy in the system is bounded, so any blowup has to be a localized singularity that blows up larger and larger in tinier and tinier spaces. But, the tinier you get in space, the larger the derivatives with respect to space get, and the harder a diffusive term (which roughly speaking adds a force counteracting large second derivatives) fights against your developing singularity.

If you'd asked me to guess last month, I would have said that I'd expected blowups were possible in the Euler equations and not in the Navier-Stokes equations. In hindsight that would have been ignorance on my part, and probably embarrassing ignorance based on Tao's attitude above, but I still think Navier-Stokes was legitimately a harder problem to solve in this way than Euler was.

LLM solutions are barely readable

Depends on the problem. The Jacobian conjecture counterexample was something a good engineering Bachelors' could understand, and even its derivation seemed like math-grad-student level, at least for students focusing on differential geometry. Very nice.

Some of the other recent big results are so unreadable that I don't even tell anyone about them until I see people are confident in the formal verification. Erdös talked about proofs being "straight from The Book" of God's best proofs of every mathematical theorem, and claimed "You don't have to believe in God, but you should believe in The Book". Humans have also come up with some devilishly-convoluted proofs over the years too, but we at least have the good taste to hate it when that happens.

it still makes sense for academics to do themselves, or at least break the model's solution into something comprehensible by humans, and receive credit for that.

I strongly agree, if by "still" you mean "in Fall 2026". Beyond that? In early 2024 I couldn't get a frontier model to integrate a reaction-convection-diffusion equation by parts and give me a weak form without making a sign error, or get it to correct its sign error without making a different error instead. In mid-2026 they're coming up with proofs "straight from The Necronomicon", but they're proofs for problems that geniuses have failed to solve for decades. At this rate I'd be very surprised if humans are better than late-2028 LLMs at finding simpler proofs or at making the proofs we do have as easy to understand as possible.

This isn't the solution, though, is it?

The academics only had a solution to a subset of Navier-Stokes, like that group. OpenAI seems to be the only ones who completed the proof, although Tao suggested the academics' solution may have extended to it given a bit more time.

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