数学家质疑 OpenAI 的 Navier-Stokes 证明与其自述方法不符
Mathematicians say OpenAI doesn't understand its own Navier-Stokes proof
数学家指出，OpenAI 关于 Navier-Stokes 问题的 166 页潜在证明与其声称沿用的 Diego Córdoba 和 Luis Martínez-Zoroa 方法只是表面相似，EPFL 数学家 Gonzalo Cao-Labora 称细节上是另一种方法。该证明用 Lean 编写，Clay Mathematics Institute 自发布起一直在审查。OpenAI 研究员 Noam Brown 表示，约 1 万个模型实例、88 小时、1300 亿 token 的智能体集群对结果的贡献并不清楚，他认为多智能体设置对成功的贡献不到 10%，真正关键的是基础模型本身。
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数学家质疑 OpenAI 的 Navier-Stokes 证明与 Córdoba 和 Martínez-Zoroa 的方法只是表面相似，并指出 1 万个智能体实例的贡献难以评估。
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In early September, the company presented a 166-page potential solution to the Navier-Stokes problem. OpenAI claimed the proof followed the approach of Spanish mathematicians Diego Córdoba and Luis Martínez-Zoroa.
Several groups of experts now disagree, Scientific American reports . "When you go to the details, it is a different approach," says mathematician Gonzalo Cao-Labora of EPFL. The similarities are only surface-level, he says.
The potential proof is written in Lean, a formal verification language that checks every logical step but doesn't make it readable to humans on its own. The Clay Mathematics Institute (CMI) has been reviewing it since its release.
Before publication, NYU mathematician Tristan Buckmaster accused OpenAI of building on work that he and Anthropic's Levent Alpöge had done with publicly available models. OpenAI denied the claim, saying it never used Buckmaster's data. A definitive answer is probably impossible because language models are black boxes, according to Camillo De Lellis of the Institute for Advanced Study.
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
There's another thing OpenAI doesn't seem to understand about its own work. The company threw a lot of compute at the effort, deploying 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. These systems are new and flashy, he says, so they get a disproportionate share of the credit. What really matters is the base model itself, which can grind through tasks over long periods and run in parallel.