数学家质疑OpenAI的纳维-斯托克斯证明
论证要点
- OpenAI声称其166页证明遵循Córdoba和Martínez-Zoroa的方法，但数学家Gonzalo Cao-Labora指出细节上方法不同，相似性仅停留在表面。
- 证明用Lean形式化语言写成，虽能验证逻辑步骤但不易读，Clay数学研究所自发布起一直在审查。
- 发布前，Tristan Buckmaster指控OpenAI基于其与Anthropic的Levent Alpöge用公开模型所做的工作，OpenAI否认，但因模型黑箱难有定论。
- 10月初OpenAI在GitHub上松散发布约400个结果，数学家难以跟进，许多人感到此举贬低了他们的领域。
- OpenAI投入约1万个模型实例、88小时和1300亿token，但研究员Noam Brown表示无人知道智能体集群具体贡献了多少。
- Brown认为多智能体设置对成功的贡献不到10%，真正重要的是基础模型本身能长时间并行处理任务。
对实践的启示
从业者应警惕将AI成果归因于炫酷的多智能体架构，实际关键可能在于基础模型能力；同时，AI生成的大量结果需谨慎对待，避免忽视领域专家的审查和贡献。
作者承认的反方与局限
OpenAI否认使用Buckmaster的数据；Brown承认多智能体贡献难以量化，但强调基础模型的重要性。
原文摘引
When you go to the details, it is a different approach.
Nobody actually knows what that massive agent swarm contributed to the result.
Brown puts less than 10 percent of the success down to the multi-agent setup.
▸ 展开原文全文（12 段，抓取自 The Decoder）
Mathematicians say OpenAI doesn't understand its own Navier-Stokes proof
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.
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