Gary Marcus 评 OpenAI 数学新成果：结果不是重点，信息不透明才是
Brief remark on OpenAI’s giant math drop - by Gary Marcus
Gary Marcus 批评 OpenAI 最新数学成果的发布方式：报告措辞含糊，未说明所用模型、具体流程、架构、失败率以及训练与数据增强细节，因此无法判断该结果在数学之外的可泛化性。他认为这类发布无法通过同行评审，社交媒体上的讨论也缺乏基本科学追问；该系统既可能是迈向 AGI 的一步，也可能只是在 Lean 与合成数据构成的可验证领域中的取巧。
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Making sense of a new regime in math
OpenAI’s massive new math drop:
[This essay was written in extreme haste before a very long wifi-less flight; please forgive typos.]
Part One: My take
The real news here isn’t the result; it’s not what we were told.
1. AI once tried to be a science. Now we get stuff like the completely vague report from OpenAI below:
“Same procedure”? “Using an unreleased model”?
This would never pass peer review.
We don’t know what the procedure was.
We know nothing about the architecture. For esxample, were the proofs generated in one shot, and then verified by the symbolic system Lean? Was there an iterative process?)
We know nothing about the failure rate. We know nothing about the training/post training/data augmention.
2. As a result, we have zero idea of how generalizable the result is outside math.
3. A lot of the discussion on social media has been reduced to an ignorant cheering section that applauds without knowing what it is applauding or what it might mean— without ever asking basic scientific questions.
The new system could be a legitimate step toward AGI. Or it could just be a clever leveraging of Lean and synthetic data in a verifiable domain with no generality whatsoever.
From the initial report, we can tell almost nothing.