It means something that is too out-of-data. For example if you try to make an LLM write a program in a strange or very new language it will struggle in non-trivial tasks.

I understand what "a new problem for an LLM is", my question is about what in the math discussion qualifies as a one.

I see references to "improvements", "optimizing" and what I would describe as "iterating over existing solutions" work, not something that's "new". But as I'm not well versed into maths I was hoping that someone that considers the thread as definite proof for that, like parent seems to be, is capable of offering a dumbed down explanation for the five year olds among us. :)

This is a useful summary given by another poster here

https://news.ycombinator.com/item?id=45833892

The novel results seem to be incremental improvements on some obscurely-named inequalities that I'm not personally familiar with, but I'm far from this field of maths

I had seen that post, and frankly it didn't pass my smell test of "LLM solves entirely new problems". But again, I'm not a mathematician to know what "unsolved problems" means in the context of the ones they gave AlphaEvolve, and more than that I'm entirely a skeptic on LLM solves problems that don't exist in their training data, so I'm biased. However the request for details is genuine. :)