It can be hard to “feel the AGI” until you see an AI surpass you in a domain you care deeply about. This week, many mathematicians and physicists at @OpenAI had their Lee Sedol moment seeing this model solve, in minutes, open problems they’d struggled with for years.
OpenAI's internal model reportedly solves Navier–Stokes in 88 hours
AISummary
Noam Brown reposted an OpenAI statement that an internal model group reached a Navier–Stokes solution in 88 hours using about 10,000 coordinating AI agents.
OpenAI said the model shows a step-function improvement on many benchmarks and that its training is ongoing, with monitoring and isolation safeguards applied throughout.
The attached chart compares GPT-6 Astra and the internal model on a curated set of open math problems across test-time compute levels, with the internal model scoring higher at each point.
AIWhy it matters
The quoted OpenAI post gives concrete figures on an internal model's Navier–Stokes result and on a benchmark comparison, showing how the model performs on open problems.
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@polynoamial
This model represents a step-function improvement on many benchmarks, and its training is ongoing. Our internal model group arrived at the Navier–Stokes solution in 88 hours, using around 10,000 coordinating AI agents. Throughout the effort, we maintained the strict safeguards—including monitoring and isolation—that we apply to all our frontier evaluations.
Source: Noam Brown · x.comPublished · added here