Maybe no human will fully understand a future proof, but they could fully understand a little piece of it. And many humans in aggregate could understand it, each with their own little piece.
Given the LLMs are struggling to explain themselves clearly, but also that these explanations cleared up somewhat by having an expert using one to query some of this research, but also the LLMs can solve problems faster than humans can read the proofs, it is possible for this category to have both examples of things that can be rendered in a human-comprehensible form, and also examples where it is not.
As an analogy: any human can check any single arithmetical calculation from a computer, that's not even particularly difficult. But a Raspberry Pi Zero can do those arithmetical calculations so fast that even if literally every single human was trained to do this at the level of the current world record holder, humanity as a whole could not keep up.
There are many problems that aren't "interesting" in a mathematical sense but can still have valuable proofs. The entire field of formal methods in software development is mostly about this kind of problem. If I want to be sure that no input can cause out-of-bounds memory access, or that my superoptimizer found the lowest possible cycle count, I don't care if it's elegant, I just need some machine-readable proof I can run through my trusted verifier.
If this is true, then what's really the point of math? A lot of math is actually useful. A proof regarding cryptography, for example, would have practical application even if not understandable by humans.