That loop involves way more flexible goal oriented attention, more intrinsic/implicit understanding of plausible cause and effect based on context, and more novel idea creation than it seems.
You can only brute force things with combinatorics and probabilities that have been well mapped via human attention, as piggy-backing off of lots of human digested data is just a clever way of avoiding those issues. Research is by definition novel human attention directed at a given area, so it can't benefit from that strategy in the same way domains which have already had a lot of human attention can.
Most innovative is derivative, either from observation or cross application. People aren't sitting in isolation chambers their whole lives and coming up with things in the absence of input.
I don't know why people think a model would have to manifest a theory absence of input.
This is by biggest issue with AI conversations. Terms like "original insight" are just not rigorous enough to have a meaningful discussion about. Any example an LLM produces can be said to be not original enough and conversely you could imagine trivial types of originality that simple algorithms could simulate (i.e. speculate on which existing drugs could be used to treat known conditions). Given the amount of drugs and conditions you are bound to propose some original combination.
People usually end up just talking past each other.