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Navier–Stokes Lost in Translation

121 points nill0 2 hours ago 84 comments

stared 1 hour ago | parent

For a refreshment of what is Navier-Stokes in a few words: https://p.migdal.pl/equations-explained-colorfully/#navier-s...

sleet_spotter 57 minutes ago | parent

This is so lovely. I desperately wish I could color code all math!!

stared 42 minutes ago | parent

You can. Not only the code is there, but also an interactive editor.

le-mark 1 hour ago | parent

> In particular, we highlight that the problem of resolving ambiguities in mathematical NL text, which is necessary in order to provide semantically faithful translation

This is what I've been wondering about with LLM proofs. Math is logical, but mathematical writing is still natural language: symbols get overloaded, conventions go unstated, and a lot rides on context. So a model can translate a statement into a formal system and prove it, and the proof can check out, while the statement it proved isn't quite the one the mathematician meant. I read this article as a caution that some of the LLM proofs announced so far may not hold up once a human checks what was actually proved. Is that a fair reading?

Edit out vulgarity

hyperpape 58 minutes ago | parent

> the downvotes will show many disagree

> gotcha bitch!

You may have misdiagnosed the problem.

ted_dunning 45 minutes ago | parent

Natural language is ambiguous, but the Lean formalization is very well defined and unambiguous.

It's not the form language that is the real problem here. It's the ambiguity on the other side and the extreme difficulty of doing a useful and accurate translation.

empath75 1 hour ago | parent

I recently spent 3 weeks with claude formalizing a CS paper about a borrow checker in lean, for a personal project.

The formalization went through, but there were _several_ mistakes in the original paper that it uncovered, from type setting errors to (many) formulas that quantified over all resources as printed, but actually applied to only arising resources in the calculus..

So the formalization did give me a formally verified borrow checker that I could use to build a programming language on top of, but it was _not_ exactly the borrow calculus that was printed in the paper.

I expect this is the most common experience when mechanizing a printed paper. There are a lot of skipped steps and handwaving.

ted_dunning 47 minutes ago | parent

This is the common experience in replicating a published paper by hand ... it is common to find "obvious" aspects that are anything but.

The scary thing is when AIs generate unreadable formal proofs and then effectively lie (or fabulate, to be polite-ish) about the natural language version of the steps. Since the natural language version is arguably the most important aspect of a solution to a flagship problem, this fabulation deflates the value of the solution while the existence of the solution discourages further work on the problem.

ndriscoll 22 minutes ago | parent

I have hopes that this is primarily a matter of needing more engineering work on ergonomic formal languages and better building a language that "looks like math." e.g. when doing linear algebra stuff, a linear combination might be defined as a finitely supported function from an index set to your space, which is fine, but ugly and maybe conceptually overwhelming on first meeting, so I did some toying with little macros and eventually a small python Lean -> HTML renderer to do some basic transformations to make it look more like typical math notation with like \Sigma_{i \in I} a_i, or with a_0+...+a_n, etc. (to... not fantastic success, but I think there's still something to the idea).

I think a lot of math notation isn't wrong given a context, so in theory we should be able to translate it into something formal. Maybe also generate living documents where you can e.g. write `h : some_claim := by details(by rw[nat_mul_comm]; ...)` and the renderer hides details just like you'd write "obviously" in a traditional text. If the reader wants, they could then expand the details. etc. I found that many codex-generated proofs could be improved by telling it that I want a sequence of steps

  have next_step := by <I don't care>
  have therefore := by <still don't care>
So that the human proof appears as the left side, and I just ignore the right side as petty details. Again, not fantastic success, but better. Otherwise it goes very... Leanish by default.

Lean's VSCode plugin is I think only starting to explore the idea of a proper IDE for math. There's probably still tons of unexplored potential for like that fused with Matlab or whatever.

dekhn 47 minutes ago | parent

As a second rate scientist, nothing makes me happier than finding a "hot" paper in my field, reading it, converting it to code, and demonstrating the authors made systematic errors that mean the paper is more likely false than true.

I've been criticized for doing this, but to me it emphasizes how much attention goes to the hot, wrong papers.

hgoel 15 minutes ago | parent

I enjoy running into those details when implementing papers, since it usually leads to improved understanding of the subject and an ability to approach the matter with more rigor in some way that I had not noticed before. It does also involve a lot of work and lost sleep though.

We should be very careful about relinquishing sorting through such details to AI.

buzzy_hacker 1 hour ago | parent

If I'm understanding correctly, this is questioning the equivalence between the natural language proof and the lean proof, but not the correctness of the lean proof?

caughtinthought 1 hour ago | parent

If the lean proof doesn't match the natural language one (which is the one the AI generated to solve the problem), it sounds like the lean proof isn't verifying the intended claim?

From the paper: "A third possibility is that the NL proof provides stronger statements than what the formal proof actually establishes, with (of course) different proofs. The latter happens in OpenAI’s announced proof of blow-up of Navier Stokes equations."

hyperpape 55 minutes ago | parent

The material is interesting, but unless the statement that is proved in lean is not blowup for Navier-Stokes, then it's still proven.

What the examples seem to show is that the proof method is different between the natural language proof and the lean proof. Which, if the lean proof actually proves blowup, would suggest that the natural language proof is subtly wrong, but the strategy was close enough to be used to create a real lean proof.

A little worrying, but part of the purpose of formalizing things in Lean, it forces you to be more accurate than natural language does. It's surprisingly common for major theorems to have slight inaccuracies early on that can be repaired. Famously, the initial proof of Fermat's Last Theorem had a flaw that took a year to repair (though I think that's unusually difficult).

So the most fundamental question is: does the Lean theorem faithfully state the right theorem?

ammar2 54 minutes ago | parent

That assumes the natural language paper came first and then was formalized in lean. I haven't looked too deeply into how these labs solve these problems (or if they even specify this publicly) but you could also start with lean and then write the natural language proof based on it.

For what it's worth the initial lean specifications for the top-level theorems generally come from human written formalizations such as in https://github.com/leanprover-community/mathlib4/blob/021ce6... so we can be reasonably confident about their correctness.

empath75 54 minutes ago | parent

> If the lean proof doesn't match the natural language one (which is the one the AI generated to solve the problem), it sounds like the lean proof isn't verifying the intended claim?

No, the other way around. The natural language proof was derived from the lean code, badly. This is my experience with using claude and lean to prove things. Its natural language explanations drift a lot from the lean, both before and after. But the lean code is the lean code.

caughtinthought 47 minutes ago | parent

That makes some sense. Given that the vast majority of math in its training data is going to be in NL/latex, I just assumed that the core reasoning happens in NL with occasional LEAN checks to ensure validity.

latent-person 30 minutes ago | parent

> The natural language proof was derived from the lean code, badly.

Was it? Are you claiming a LLM does reasoning in lean or what? Since this (and all the other proofs by OpenAI etc) have been in the reverse order [1]:

> The agents arrived at their resolution on Saturday, September 5, about 88 hours after the first agents were launched. Lean formalization and verification took an additional 17 hours via GPT‑6 Astra.

[1]: https://openai.com/index/navier-stokes-solution/

caughtinthought 28 minutes ago | parent

Yeah, I was surprised some people think LLMs are reasoning in Lean directly... all their training data is in NL.

ammar2 16 minutes ago | parent

It's not that much of a stretch: give the LLM a top-level proposition for the thing you want to prove and have it hack away at it. Each sub-step is verified in lean so you know it's correct. But, the linked post definitely suggests otherwise.

That is definitely interesting because how do you know the 88 hours of work are correct before you throw another 17 hours of lean formalization work on it? You could end up just finding out there was some hallucination in the original work.

OrderlyTiamat 57 minutes ago | parent

The lean proof being correct is easy to verify, whether it proves the thing we care about is much harder.

If your code compiles, are you sure it's bug free?

jansport123 47 minutes ago | parent

syntax vs semantics

ndriscoll 38 minutes ago | parent

I'm pretty sure Mathlib has had enough human authored definitions to formalize the basic calculus necessary to state Navier-Stokes for quite some time? Some other problems admittedly need quite a bit of machinery built up to even try to say what the question is, but every undergrad learns multiple approaches to formally define everything necessary to write down a PDE.

nyeah 9 minutes ago | parent

[delayed]

empath75 57 minutes ago | parent

Yes, exactly. There's no real pressure on AI to get the natural language version of the proof correct, and no way to really judge it automatically.

zmgsabst 53 minutes ago | parent

Yes — because there are many non-equivalent statements that are easier to prove.

So the Lean proves something and the question is whether that something is actually what we care about — or something similar, but ultimately not the question.

jrflo 47 minutes ago | parent

It doesn't look like they've found an error in the NL proof either, just that they are different?

kccqzy 35 minutes ago | parent

Indeed. The natural language proof is incorrect but the Lean proof is correct.

Humans have made similar mistakes too. A human writes a specification for how things should work, the human translates that into code, the code does not work, and finally the human fixes the code and forgets to fix the original spec.

kurtis_reed 25 minutes ago | parent

How do you know the natural language proof is incorrect?

kurtis_reed 22 minutes ago | parent

Yes however, whether a natural language proof and a formal proof "correspond" is subjective.

ballmerpoint 59 minutes ago | parent

This shouldn’t be a surprising result. We’ve known almost since LLMs became a thing that they can “prefer” modifying the terms or context of a problem when they can’t solve it directly (what one might call “cheating” if there were any volition involved). Often that happens in a way that isn’t immediately obvious to the user.

Before it was dropping databases or deleting repositories. Now it’s subtly changing the meaning of math problems to get a correct but irrelevant answer.

ForHackernews 51 minutes ago | parent

Indeed. I've never used AI to translate between natural language and Lean but I have gone from English to Golang, Python, Typescript and SQL and its interpretations can be... creative, let's say.

sebzim4500 41 minutes ago | parent

No one is disputing the correctness of the lean proof, the problem is that they did a bad job converting it to natural language.

j2kun 58 minutes ago | parent

I think this highlights that, at the very least, coverage of AI-generated proofs should describe them as "claims" to solve problems, until, like all other works, the community has had time to review and digest them.

The idea that an AI company is beyond peer review is harmful.

john_strinlai 52 minutes ago | parent

>The idea that an AI company is beyond peer review is harmful.

i havent seen this sentiment expressed anywhere, have you?

isn't this comment chain on a submission about openai's claims being reviewed?

abdullahkhalids 40 minutes ago | parent

OpenAI has expressed this sentiment by not submitting to or saying they will submit their results to peer reviewed journals.

setgree 36 minutes ago | parent

"not interested in" != "beyond"

john_strinlai 35 minutes ago | parent

not submitting to whatever journal is quite different than saying they are "beyond peer review"

are people not reviewing openai claims right now?

yieldcrv 35 minutes ago | parent

Because they want to release everything on github so everyone can peer review it themselves

This is far more efficient and they’re telling the academic industry to grow up

Sister comments are saying that academics dont like the Lean programming language and see a lack of human language described proof. Doesn’t sound like something I should care about but I’m watching for a better human language description of the problem as this discussion evolves

fasterik 33 minutes ago | parent

I would say it's released in the spirit of open source. "Peer review" in the narrow sense exists primarily to assign prestige in academia; but there's nothing stopping anyone from "peer reviewing" the GitHub repository.

j2kun 28 minutes ago | parent

I would say it's released in the spirit of machine learning's competitive landscape (which is the culture this emerged from).

1234-1298 13 minutes ago | parent

So they could also dump a 100 quadrillion line proof in Bourbaki notation and call it a day?

The proof was released in the spirit of being first at all costs without any attempt to clean it up. I doubt that OpenAI mathematicians could give a coherent talk about it, certainly not using a blackboard.

abdullahkhalids 10 minutes ago | parent

This is an equivalent of a company producing security software, open sourcing their code, and then claiming that since no one has found any serious bugs, their software is secure.

No. The way to build confidence that your software is well made, you do a proper external security audit and obtain the requisite certificate from a proper auditing firm.

It's also incorrect to think peer review in mathematics is low quality (like it is in some other fields). Certainly, when major results are in place, editors ensure that high quality peer reviewers are recruited and do their job properly. Like all human processes this fails sometimes, but not enough to not do it.

swiftcoder 38 minutes ago | parent

I've seen a lot of breathless reporting about various mathematical things being "proven" on the basis of the LLM-generated Lean formulation compiling. We probably wouldn't declare that for a human-written proof until peers had checked the proof for errors

j2kun 32 minutes ago | parent

Exactly. Coverage here is "OpenAI has solved problem X", not "OpenAI has claimed to solve problem X."

fatcatsbestcats 32 minutes ago | parent

This. The proof of Fermat’s Last Theorem took 15+ months to check. It’s absurd to see the media reporting that these big problems are solved based off of a news release and a hastily and mostly AI-written manuscript, and OpenAI et al. are all too happy to run with said breathless reporting.

john_strinlai 16 minutes ago | parent

there's breathless reporting of just about everything scientific. physics, astronomy, archaeology, etc. have this sort of thing all the time.

yet i have never seen anyone say "the idea that physicists are beyond peer review is harmful" because some mainstream news articles published a piece about dark energy or whatever.

fasterik 40 minutes ago | parent

As far as I understand it, nobody is disputing the correctness of the Lean proof, or that it proves the conjecture it actually claims to prove. That's sufficient to consider the problem "solved". The natural language proof is a "nice to have".

Arodex 32 minutes ago | parent

>we provide several examples of AI mistranslations of NL statements and proofs into Lean in practice, resulting in mismatches between NL proofs and their Lean `verifications'. These include OpenAI's announced Navier-Stokes proof. In particular, we show that the formalised Lean proof does not correspond to the NL proof of blow-up of solutions to the Navier-Stokes equations.

Maybe read the original article before replying, at a minimum.

j2kun 30 minutes ago | parent

Both proofs may be correct, and the problem may indeed be solved. My point is that it should not be assumed.

fasterik 27 minutes ago | parent

Does that contradict what I said? Right there in your quote, it says that the NL proof does not correspond to the Lean proof. However, the statement of the theorem in Lean is independent from the NL proof. It comes from DeepMind's formalization, which as far as I'm aware nobody disputes.

kurtis_reed 26 minutes ago | parent

> Maybe read the original article before replying, at a minimum.

Maybe read the comment before replying, at a minimum.

abstrakraft 28 minutes ago | parent

The claim in TFA is that the formalization(in Lean) of the problem does not correspond to the natural language statement of the problem, such that the statement proven is not the conjecture for which proof is required for the problem to be considered "solved".

fasterik 21 minutes ago | parent

That's not the claim made in TFA. See the sibling comments, in particular about the DeepMind formalization.

jrflo 52 minutes ago | parent

So my guess is that they have the AI system attempt to prove the theorem in natural language, then try to generate a Lean proof for it, and in that process they end up with a slightly different solution as the autoformalizer is essentially rewriting the NL proof to make it formalizable? Do we just need a "reverse pass" to re-align the NL proof with the Lean code?

Also, it doesn't seem that they are questioning the truthfulness of either proof, just that they are different?

ted_dunning 40 minutes ago | parent

Generating the lean proof first is a viable approach as well followed by an explanatory pass.

Actually, they are questioning whether the natural language description of the proof is either not faithful to the formal proof, or simply wrong, or both.

FrustratedMonky 50 minutes ago | parent

Not a mathematician. Why not just always use LEAN? Why use natural language at all?

jansport123 45 minutes ago | parent

Same reason humans write code not only for a compiler to translate into machine code but also so other humans can understand what we write, learn from it, modify it etc...

Jaxan 41 minutes ago | parent

Not only that, we also have code comments and standalone documentation.

binlog 44 minutes ago | parent

Because people need to understand what is being proven.

ted_dunning 44 minutes ago | parent

Because it is really hard to read and the level of detail is so high that even lemmas that you can read may have such enormous levels of detail that makes real understanding difficult given that humans have limited working memory.

matusp 42 minutes ago | parent

Why not always write machine code? Why use programming languages at all?

Jtarii 42 minutes ago | parent

Lean is a write only programming language.

caughtinthought 32 minutes ago | parent

The example in Figure 1 should help understand why... the NL version is much more approachable for humans.

arbirk 43 minutes ago | parent

It was a piston in a non-compressible fluid so to speak (ie. storm in a glass of water)

notrealyme123 31 minutes ago | parent

I get the feeling a lot of people propose that we can write a verifier for every proof in lean.

Can someone tell me in simple terms why this doesn't conflict with the incompleteness theorems?

edit: thanks for the responses, i feel slightly less dumb now

ezwoodland 28 minutes ago | parent

Just all the useful proofs. You can get arbitrarily more complicated and uninteresting theorem statements by making meta statements about the system you are doing proofs in. At some level the system can't answer questions about itself.

hypersoar 28 minutes ago | parent

The incompleteness theorem says that there are statements which can be neither proven true nor false in a given axiomatic system. If there is a proof to write in lean, then the statement is already outside the bounds of incompleteness.

skywalqer 25 minutes ago | parent

Well, I believe the incompleteness theorems speak about provability, not about how the proofs themselves are expressed.

We know as a consequence of Goedel theorems (at least I believe so), that there is no algorithm that would take a statement and output a proof if it is provable or a counterexample if it is not. However, AI provers never give anything for sure, so I think there is no contradiction here.

jcranmer 24 minutes ago | parent

The incompleteness theorems state that every sufficiently complicated logic lets you construct a statement that is effectively "this statement has no proof," so either there exists true statements that lack proofs (incompleteness) or there exists false statements with proofs (incorrectness).

ComplexSystems 30 minutes ago | parent

Aside from the usual squabbling about AI, it seems the bombshell claim is this:

"In particular, we show that the formalised Lean proof does not correspond to the NL proof of blow-up of solutions to the Navier-Stokes equations."

So these authors seem to be claiming that OpenAI has not really proven Navier-Stokes at all. If I get their idea correctly, they are claiming that the LLM has not formalized the original "natural language" idea of Navier-Stokes correctly. If true, it would mean that their purported Lean proof is not actually a proof of Navier-Stokes at all, but something that is an incorrect translation of the original natural language idea. If correct, this is a really bold claim and I would like to see if other researchers agree.

pohl 27 minutes ago | parent

> has not formalized the original "natural language" idea of Navier-Stokes incorrectly

Did you mean “not…correctly”?

omnicognate 25 minutes ago | parent

If I understand the abstract correctly (big caveat), they aren't saying they didn't prove it. They're saying they gave two proofs, one in natural language and one in Lean, that are not equivalent to each other. I assume the main significance is that the Lean proof is not a formal verification of the natural language one and the natural language proof is not a readable explanation of the Lean one. Both of those things can be desirable, so to complete the set we'd get 4 proofs.

kzrdude 20 minutes ago | parent

From computer science perspective the conclusion is obvious: untenable to have two representations without an exact translation or machine checked correspondence between then. All we have is a vibe translation using the LLM. The methodology should obviously be improved.

TeMPOraL 8 minutes ago | parent

But just to clarify: is either of them actually addressing the real Navier-Stokes, or will it turn out we'll end up with two pairs of proofs about something irrelevant to the actual problem?

nyeah 6 minutes ago | parent

[delayed]

nicf 23 minutes ago | parent

I read them as making a much weaker claim than this: not that the Lean proof isn't valid, just that it is not actually a formalization of the natural-language proof in the PDF they provided alongside it. I haven't heard any PDE people claim that the Lean proof is invalid, and I have heard things from a lot of them that imply that they think it is valid. (I'm a former research mathematician, but this is very far from my specialty, so I'm not really equipped to evaluate this claim myself.)

mkarrmann 19 minutes ago | parent

No, they're not claiming that.

No one is disputing that the Lean formaization of Navier-Stokes is correct, so we should have high confidence that the generated Lean proof is valid.

The authors are claiming that the Lean proof is not the same proof as the NL one. Therefore, we shouldn't yet have confidence that the NL proof is valid.

This is an important claim which the math community will need to work through. However, the Lean proof alone is sufficient for OpenAI to (reasonably confidently, leaving aside questions of academic manners) claim to have proven NS.

fasterik 10 minutes ago | parent

The claim is about the equivalence between two proofs and says nothing about the correctness of either proof. This seems to be confusing a lot of people.

129983-asf 26 minutes ago | parent

Two leading experts on Navier Stokes still do not know whether their methods were used:

https://terrytao.wordpress.com/2026/10/04/on-classical-solut...

Humans will have to wade through mountains of slop to decipher the argument. Alternatively, they could just ignore it like Mochizuki's ABC proof prior to the Scholze/Stix refutation.

Sniffnoy 21 minutes ago | parent

Hm, looking through here, I don't see where they state what it is that OpenAI actually proved instead of Navier-Stokes blowup with forcing. I see where they do this for some other particular statements used along the way, but not for the headline result.

dooglius 8 minutes ago | parent

Given the high-level description of the examples, I think it's less of a "mis-translation" as it is the LLM tweaking the proof as it formalized it. Going between m+4 and m+5 is a pretty different thing than the sort of ambiguities that generally arise in parsing natural-language mathematical statements.

sigbottle 4 minutes ago | parent

Will we ever run into a theory of meaning crisis?

_Assuming_ two failure modes:

- The lean kernel could always have a bug. - The formalized statement may not correspond to what _mathematicians_ "actually wanted"

It seems natural to make the argument of, "Well, even if you make the argument that the proof can have mistakes, it's surely easier to check the problem statement of something rather than the solution".

(A "nice property" is that, the agent doesn't need to even get "subarguments correct" according to the _second_ criteria - maybe in the natural proof it invents an object subtly different from the formal one, but it all checks out. If you guarantee that the _original_ statement corresponds, then the only possibility is the lean kernel. So it doesn't recurse infinitely, in this case).

But "definitions" are always a really weird thing that I don't think we have good theories for? How do you quantify how much descriptive power you need to express a question? Often times in math, the hard part is getting the definition right - but what if the definition itself starts to become so complex and unverifiable that no one can correspond that to anything? Well, it seems like many interesting long-standing math problems have "relatively" simple problem statements, in such a way that you could formalize it to lean easily, but not sure if there's really a silver bullet w/ lean or if it's going to be turtles all the way down.

It probably doesn't matter as long as AI keeps skyrocketing on the much more general property that is "intelligence", but still. Interesting to think about.

(Well, this is where AIT gets actually interesting, but still, I don't think its a generalized theory of semantics.)