OpenAI 最新争议揭示数学研究的未来走向

该文件所传递的明确暗示是，OpenAI 的模型以某种方式利用了 Buckmaster 和 Alpöge 的研究成果。乍看之下，这种情况是可信的。Buckmaster/Alpöge 与 OpenAI 的证明都采用了一种研究纳维-斯托克斯问题的方法，该方法由数学家 Diego Córdoba 和 Luis Martínez-Zoroa 开创。

布朗大学数学教授 Javier Gómez-Serrano 表示，这种方法是被认为有望解决纳维-斯托克斯问题的多种路径之一。因此，尽管两个团队独立得出这一方法绝非不可能，但 Buckmaster 和 Alpöge 的研究影响了 OpenAI 的可能性同样存在。

在新闻发布会上，OpenAI 首席研究官 Mark Chen 再次否认任何 Agent 或 OpenAI 员工曾访问 Buckmaster 和 Alpöge 的论文草稿记录；但鉴于有关 Hugging Face 黑客事件的披露，很明显，OpenAI 并不总能完全掌握其 Agent 正在做什么。

如果 OpenAI 的模型确实在 Buckmaster 和 Alpöge 的工作上训练过，或者其 Agent 以某种方式获得了相关内容，那么该公司未能查明真相、并给予这些研究者应有的署名，这将有损其声誉。但对于数学家而言，这一版本的故事或许也有一线积极意义：它表明，两名人类研究者的艰苦工作——其中一人还是纳维-斯托克斯问题领域的知名专家——对于这些 Agent 解决该千禧年难题的能力至关重要。

专家长期以来一直认为，“研究品味”，即选择有前景的研究问题和研究方向的能力，是 AI 在科学和数学领域面临的重大障碍。如果 OpenAI 的 Agent 的确是因为 Buckmaster 和 Alpöge 也选择了 Córdoba–Martínez-Zoroa 路径，才决定沿着这一方向推进，那么人类的研究品味就在 OpenAI 的成功中发挥了关键作用。

即便如此，更宏观的图景仍令人警醒。Buckmaster 和 Alpöge 与公开可用模型合作近一年所取得的进展，说明了人机协作的潜力；但他们未能实现完整解答。与此同时，OpenAI 使用内部模型在数天内通过暴力计算得出了解决方案，而这一成功的代价高得惊人：Bubeck 和 Chen 在新闻发布会上表示，团队只能通过同时运行约 10,000 个 Agent 来解决该问题，成本达数百万美元。

The clear implication of the document is that OpenAI’s models somehow made use of Buckmaster and Alpöge’s work. That scenario is plausible on its face. The Buckmaster/Alpöge and OpenAI proofs both make use of an approach to the Navier-Stokes problem pioneered by the mathematicians Diego Córdoba and Luis Martínez-Zoroa.

According to Javier Gómez-Serrano, a mathematics professor at Brown University, this approach was one of several that was thought to hold promise for solving the Navier-Stokes problem. So, while it’s by no means impossible that both teams could have arrived at this approach independently, it’s also conceivable that Buckmaster and Alpöge’s work could have influenced OpenAI’s.

In the press briefing, Mark Chen, OpenAI’s chief research officer, again denied that any agents or OpenAI employees accessed Buckmaster and Alpöge’s transcripts—but given what has been revealed about the Hugging Face hack, it’s clear that OpenAI is not always entirely aware of what its agents are doing.

If OpenAI’s models did train on Buckmaster and Alpöge’s work, or if its agents somehow gained access to it, then the company’s failure to track down the truth and assign those researchers appropriate credit reflects poorly on it. But there might be a thin silver lining to that version of the story for mathematicians, because it would suggest that the hard work of two humans, one of whom is a prominent expert on Navier-Stokes, was essential to the agents’ ability to solve the Millennium Problem.

Experts have long identified “research taste,” or the ability to choose promising research questions and directions, as a major obstacle for AI in science and mathematics. If the OpenAI agents did indeed choose to follow the Córdoba–Martínez-Zoroa approach because Buckmaster and Alpöge had done the same, then human research taste played an essential role in OpenAI’s success.

Even so, the bigger picture here is sobering. The progress that Buckmaster and Alpöge made over almost a year of collaboration with publicly available models speaks to the promise of human–AI collaboration. But they were not able to achieve a full solution. Meanwhile, OpenAI brute-forced a solution in a few days using an internal model, and their successful solution came at an astronomical cost: In the press briefing, Bubeck and Chen said the team was only able to solve the problem by running about 10,000 agents concurrently, at a cost of millions of dollars.