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But if a LLM writes such a paper, how do you know there aren't any issues in it that cause wrong probabilities/distributions?

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You don't, that's why you review it (just how you would handle a real human submitting a paper). The main issue with LLM papers is that they tend to be very dense/circuitous or—because LLMs don't truly understand what they're doing—have the wrong focus. An LLM might spend pages on a trivial result, but quickly gloss over a truly remarkable finding.

Terry Tao talks about this challenge in some of his blog posts and interviews, it's pretty interesting.

That was adamddev1s point. It's a unreliable tool. You cannot copy & paste its results into a paper as proof. You could do this with e.g. Lean. To review what it writes you need to understand the topic to such an extend that you could have written it yourself (at least that is how it is for me when I review code).

Yes, for inspiration or search they are great tools.

>You don't, that's why you review it

Review enough of those without doing the proving yourself, and you'll soon lose the ability to review. And have LLMs do the course work for you at math school, and you might never acquire such ability to begin with anyway.

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