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.