OpenAI released a 166-page potential solution to the Navier-Stokes problem in early September, claiming it followed the approach of Spanish mathematicians Diego Córdoba and Luis Martínez-Zoroa. Several expert groups now dispute this claim, according to Scientific American.
Mathematician Gonzalo Cao-Labora of EPFL said the similarities between OpenAI's proof and the approach of Córdoba and Martínez-Zoroa are only surface-level. "When you go to the details, it is a different approach," he said. The proof is written in Lean, a formal verification language that checks logical steps but does not make it readable to humans on its own.
The Clay Mathematics Institute has been reviewing the proof since its release. Before publication, NYU mathematician Tristan Buckmaster accused OpenAI of building on work he and Anthropic's Levent Alpöge had done with publicly available models. OpenAI denied the claim, saying it never used Buckmaster's data.
In early October, OpenAI dumped roughly 400 more results that the math community can barely keep up with. Publishing them as a loose collection on GitHub angered many mathematicians, who felt it cheapened their field. Nobody knows if 10,000 AI agents actually helped.
OpenAI deployed about 10,000 model instances for roughly 88 hours and 130 billion tokens to develop the potential Navier-Stokes solution. According to OpenAI researcher Noam Brown, nobody actually knows what that massive agent swarm contributed to the result.
Behavior at that scale hasn't been studied because running systematic comparison tests with thousands of agents costs too much.
No one has tested how long a single agent would take to solve the same problem, and it's nearly impossible to tell what concrete advantage 10,000 agents offer over 1,000.
Brown puts less than 10 percent of the success down to the multi-agent setup.
Source: thedecoder
