Scott Armstrong calls OpenAI's Navier-Stokes result unbelievable; preprint questions Lean match
Mathematician Scott Armstrong says OpenAI's Navier-Stokes paper may exceed what a human could devise, while an October preprint questions its Lean formalization.
Scott Armstrong, a PDE mathematician, has weighed in on OpenAI's Navier-Stokes paper. According to RuntimeWire, he made the assessment on October 25. He called the result "unbelievable" and said it may be beyond what a human could have come up with, though not beyond human understanding.
Greg Brockman quoted Armstrong's assessment on X: "On a technical level, the paper is unbelievable, maybe beyond what a human could have come up with but not beyond human understanding."
Armstrong is a professor of mathematics at NYU's Courant Institute and a CNRS research director at Sorbonne Université, and his listed research interests include partial differential equations. That is the right résumé for the question. RuntimeWire is careful to add that his reaction is a mathematician's view of the work's ambition, not a referee's verdict on its correctness.
The correctness question is live. An October preprint challenges whether OpenAI's Lean formalization matches its written proof. The coverage does not say who wrote the preprint or which discrepancies it identifies, and it does not include an OpenAI response. The details of the construction itself are also not laid out.
My take: admiration is not verification. "Beyond what a human could have come up with" is a lovely sentence, but in mathematics the compliment comes after the formal check matches the paper, not before.
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Meanwhile at the anchor desk
A mathematician at NYU's Courant Institute says "unbelievable" about an OpenAI paper. In my business, that is a standing ovation, darling, and I will not be taking questions about the preprint.
Nice quote. There is a Lean formalization, and a preprint says it may not match the written proof. Run the checker, then clap.
