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A claimed mismatch between OpenAI's announced Navier-Stokes proof and its Lean formalization

The paper's authors say the Lean-verified formal proof does not correspond to OpenAI's announced natural-language Navier-Stokes argument.

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Andreas Kirsch 🇺🇦AK
12 Sources, 2d ago, first seen 2d ago

TLDR

In a paper submitted October 6, researchers argue that a Lean-verified formal proof does not correspond to the natural-language argument in OpenAI's announced proof of Navier-Stokes blow-up. They warn that AI translation of mathematical text into a formal language can introduce mismatches, so verifying the formal version need not validate the original argument.

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12 Sources, first seen 2d ago

6.8K likes267 comments2.1K saves1.5K reposts

Combined views

780.4K

12 Sources, first seen 2d ago

6.8K likes267 comments2.1K saves1.5K reposts

A preprint submitted October 6 by Alexander Bastounis, Fabian Circelli and Anders C. Hansen challenges the relationship between a natural-language mathematical argument and a Lean formalization presented as its formal verification. The authors argue that the formalized proof associated with OpenAI's announced work on Navier–Stokes blow-up does not correspond to the argument written in natural language.

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The distinction matters because a proof assistant checks the formal statement it receives. Before that can happen, an autoformalization system must translate ordinary mathematical prose into definitions and claims expressed in a formal language. A mechanically valid Lean proof shows that the formal version follows from its stated premises, but it does not by itself establish that the translation preserved the meaning of the original text.

Where verification can diverge

The authors argue that ambiguity in mathematical language makes faithful translation unusually difficult. Their paper places the problem of resolving those ambiguities arbitrarily high in the Solvability Complexity Index hierarchy and informally describes semantically faithful autoformalization as harder than any computational problem, including the Halting problem. That is the authors' theoretical claim in a new preprint, not an independently established verdict on OpenAI's underlying mathematics.

To illustrate the practical risk, the paper says it found several cases in which AI translations of natural-language statements and proofs into Lean produced meanings that did not match the original prose. Its examples include the announced Navier–Stokes argument, for which the authors say the verified formal proof and written proof do not correspond. The issue they identify is therefore not that Lean incorrectly checked a formal proof, but that the formal object may not represent the intended claim.

One proposed safeguard is to make the translation easier to inspect from both directions. Researcher Andreas Kirsch suggested moving from an informal sketch to Lean and then back-translating the formal proof into natural language for comparison. The suggestion offers an additional comparison step, though it does not by itself resolve the preprint's theoretical objection.

The narrower takeaway is that formal verification and faithful formalization are separate steps. A proof assistant can certify the formal argument it is given; whether that argument captures the source text still requires its own scrutiny.

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Useful links

arXiv.org

Navier-Stokes lost in translation: Why Lean verification of AI...

Thinking In Math · YouTube

What Did OpenAI Actually Prove About Navier–Stokes? (And What It Didn't)

OpenAI

On the Navier–Stokes Millennium Prize Problem

AIOversimplified · YouTube

25 Fields Medalists Signed a Letter About This. It Never Mentions OpenAI.

GitHub

GitHub - francescoantoniodeluca/navier-stokes-formal-audit: Independent, reproducible audit of OpenAI's Lean formalization of Navier–Stokes alternatives (C) and (D), including Comparator, nanoda, kernel verification, and an independent bridge to the Clay statement.

Sentiment

Positive——Negative

Summary

Not enough discussion yet.

No sentiment analysis available yet.

Useful Links

arXiv.org

Navier-Stokes lost in translation: Why Lean verification of AI...

OpenAI

On the Navier–Stokes Millennium Prize Problem

GitHub

GitHub - francescoantoniodeluca/navier-stokes-formal-audit: Independent, reproducible audit of OpenAI's Lean formalization of Navier–Stokes alternatives (C) and (D), including Comparator, nanoda, kernel verification, and an independent bridge to the Clay statement.

Related Videos

  • What Did OpenAI Actually Prove About Navier–Stokes? (And What It Didn't)Thinking In Math · YouTube
  • 25 Fields Medalists Signed a Letter About This. It Never Mentions OpenAI.AIOversimplified · YouTube

Useful Links

arXiv.org

Navier-Stokes lost in translation: Why Lean verification of AI...

OpenAI

On the Navier–Stokes Millennium Prize Problem

GitHub

GitHub - francescoantoniodeluca/navier-stokes-formal-audit: Independent, reproducible audit of OpenAI's Lean formalization of Navier–Stokes alternatives (C) and (D), including Comparator, nanoda, kernel verification, and an independent bridge to the Clay statement.

Related Videos

  • What Did OpenAI Actually Prove About Navier–Stokes? (And What It Didn't)Thinking In Math · YouTube
  • 25 Fields Medalists Signed a Letter About This. It Never Mentions OpenAI.AIOversimplified · YouTube

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13 Sources

arXiv.orgNavier-Stokes lost in translation: Why Lean verification of AI...
Pedro Domingos@pmddomingosSurprise: the verified Navier-Stokes proof doesn’t match the one in the paper. https://arxiv.org/abs/2610.081442d
David Pfau@pfauRT @Quasilocal: The plot thickens. "We show that the formalised Lean proof does not correspond to the [written paper] proof of blow-up of…2d
Mathieu@miniapeur👀👀 "[…] we provide several examples of AI mistranslations of natural language (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." https://arxiv.org/abs/2610.081442d
Andreas Kirsch 🇺🇦@BlackHCI think we have to go from informal sketch to formal Lean and then crucially to backtranslated natural language proof. It's easier to go from formal to informal faithfully than the other way around1d
Valerio Capraro@ValerioCapraroBREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification. The most important article to read today is not one of OpenAI’s 700 AI-generated math papers. It is this other paper, making a deep and worrying point: A Lean-verified proof does not automatically validate the proof written in natural language, nor does it mean that the formal statement captures the intended theorem. During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely. It can hallucinate another theorem. Lean correctly verifies the result. But the proved result may no longer be what the paper claims. This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution. They identify at least two mismatches between the written intermediate results and their Lean counterparts: One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result. A pressure-flux estimate is obtained through a different bound, and proved through a different argument. It is not clear whether these mismatches invalidate the entire proof. But they raise an important issue. OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving. Epistemia at scale. * Paper in the first reply1d
Kyle Cranmer@KyleCranmerRT @ValerioCapraro: BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification. The most important article to read…1d
Mariya I. Vasileva@mariyaivasilevaRT @miniapeur: 👀👀 "[…] we provide several examples of AI mistranslations of natural language (NL) statements and proofs into Lean in practi…1d
Ravid Shwartz Ziv@ziv_ravidOpenAI spent 130 billion tokens and 10,000 agents on AI slop-proofing?!?!? 🤣1d
Robert Joseph@RobertljgI would like to add some points here: )! 1 - Autoformalization faithfulness is a real problem, several academic teams (ours included) work on it, and I agree with the paper's premise. But a few things are worth adding. 2 - The paper's two mismatches sit inside intermediate lemmas, not the final theorem. The final statements pass Comparator, the @leanprover FRO tool that checks a proved theorem is exactly the stated problem, using only the standard axioms. 3 - And that statement was not written by @OpenAI's model. It is the Navier–Stokes statement from @GoogleDeepMind Formal Conjectures, added by Tomáš Skřivan in May 2026 (I contributed a small PR to that file as well). The paper never mentions it. That matters, because formalizing the statement and formalizing the proof are different questions. The statement was written and reviewed by people in public. Only the proof was produced by the model, and the kernel checks the proof. 4 - Lemma 8.6: Lean assumes 5 bounded derivatives where the paper uses 4, of a function already assumed C^∞, so it costs nothing. Lemma 10.5: same conclusion, the two solutions agree, with a different inequality in the middle. Neither is a gap in the paper from what I see. The m+4 bound is a short Fourier estimate, and (10.19) uses the classical L^{3/2} bound for Riesz transforms, which Mathlib does not have yet. The model took another route where the library lacked the tool, which is expected at times: )! 5 - To be fair to OpenAI, this is not a mistake. A formal proof almost always reroutes some steps. What's missing is a note in the paper saying where. The fix is cheap: after formalizing, have the agent compare the Lean with the paper and update the paper where the routes differ. The English proof still deserves peer review like any paper, but Lean tells us the theorem is true. "Lean checked a proof, not the paper's proof" is fair. "The verified proof is wrong" is not what was shown: ) just to clarify!1d
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    13 Sources

    arXiv.orgNavier-Stokes lost in translation: Why Lean verification of AI...
    Pedro Domingos@pmddomingosSurprise: the verified Navier-Stokes proof doesn’t match the one in the paper. https://arxiv.org/abs/2610.081442d
    David Pfau@pfauRT @Quasilocal: The plot thickens. "We show that the formalised Lean proof does not correspond to the [written paper] proof of blow-up of…2d
    Mathieu@miniapeur👀👀 "[…] we provide several examples of AI mistranslations of natural language (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." https://arxiv.org/abs/2610.081442d
    Andreas Kirsch 🇺🇦@BlackHCI think we have to go from informal sketch to formal Lean and then crucially to backtranslated natural language proof. It's easier to go from formal to informal faithfully than the other way around1d
    Valerio Capraro@ValerioCapraroBREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification. The most important article to read today is not one of OpenAI’s 700 AI-generated math papers. It is this other paper, making a deep and worrying point: A Lean-verified proof does not automatically validate the proof written in natural language, nor does it mean that the formal statement captures the intended theorem. During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely. It can hallucinate another theorem. Lean correctly verifies the result. But the proved result may no longer be what the paper claims. This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution. They identify at least two mismatches between the written intermediate results and their Lean counterparts: One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result. A pressure-flux estimate is obtained through a different bound, and proved through a different argument. It is not clear whether these mismatches invalidate the entire proof. But they raise an important issue. OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving. Epistemia at scale. * Paper in the first reply1d
    Kyle Cranmer@KyleCranmerRT @ValerioCapraro: BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification. The most important article to read…1d
    Mariya I. Vasileva@mariyaivasilevaRT @miniapeur: 👀👀 "[…] we provide several examples of AI mistranslations of natural language (NL) statements and proofs into Lean in practi…1d
    Ravid Shwartz Ziv@ziv_ravidOpenAI spent 130 billion tokens and 10,000 agents on AI slop-proofing?!?!? 🤣1d
    Robert Joseph@RobertljgI would like to add some points here: )! 1 - Autoformalization faithfulness is a real problem, several academic teams (ours included) work on it, and I agree with the paper's premise. But a few things are worth adding. 2 - The paper's two mismatches sit inside intermediate lemmas, not the final theorem. The final statements pass Comparator, the @leanprover FRO tool that checks a proved theorem is exactly the stated problem, using only the standard axioms. 3 - And that statement was not written by @OpenAI's model. It is the Navier–Stokes statement from @GoogleDeepMind Formal Conjectures, added by Tomáš Skřivan in May 2026 (I contributed a small PR to that file as well). The paper never mentions it. That matters, because formalizing the statement and formalizing the proof are different questions. The statement was written and reviewed by people in public. Only the proof was produced by the model, and the kernel checks the proof. 4 - Lemma 8.6: Lean assumes 5 bounded derivatives where the paper uses 4, of a function already assumed C^∞, so it costs nothing. Lemma 10.5: same conclusion, the two solutions agree, with a different inequality in the middle. Neither is a gap in the paper from what I see. The m+4 bound is a short Fourier estimate, and (10.19) uses the classical L^{3/2} bound for Riesz transforms, which Mathlib does not have yet. The model took another route where the library lacked the tool, which is expected at times: )! 5 - To be fair to OpenAI, this is not a mistake. A formal proof almost always reroutes some steps. What's missing is a note in the paper saying where. The fix is cheap: after formalizing, have the agent compare the Lean with the paper and update the paper where the routes differ. The English proof still deserves peer review like any paper, but Lean tells us the theorem is true. "Lean checked a proof, not the paper's proof" is fair. "The verified proof is wrong" is not what was shown: ) just to clarify!1d
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