However, it is impractical to think consciously of all possible variations. So the brain only surfaces ones likely to be useful. This is the role an LLM plays here.
An expert or an LLM with more relevant experiences would be better at suggesting these variations to try. Chess grandmasters often don’t consciously simulate more possibilities than novices.
We can imagine LLMs trained in mathematical programming or a different domain playing the same role more effectively in this framework.
https://github.com/google-deepmind/funsearch/blob/main/bin_p...
These aren't complex or insightful programs - they're pretty short simple functions, of the sort you typically get from program evolution. The LLM's role here is just proposing edits, not leveraging specialized knowledge or even really exercising the limits of existing LLMs' coding capabilities.
A fun story from a UCLA math PhD: “terry tao was on both my and my brother's committee.
he solved both our dissertation problems before we were done talking, each of us got "wouldn't it have been easier to...outline of entire proof"”
https://twitter.com/AAAzzam/status/1735070386792825334
Current LLMs are far from Terence Tao but Tao himself wrote this:
“The 2023-level AI can already generate suggestive hints and promising leads to a working mathematician and participate actively in the decision-making process. When integrated with tools such as formal proof verifiers, internet search, and symbolic math packages, I expect, say, 2026-level AI, when used properly, will be a trustworthy co-author in mathematical research, and in many other fields as well.”
https://unlocked.microsoft.com/ai-anthology/terence-tao/