LLMs are inexplicably good at working within any tight feedback loop to coerce the desired solution. This is precisely why proof assistants + LLMs are non intuitively successful.
This is also why they're so good at creating three.js or Blender work when the output is so easily constrained to "Look exactly like that". I recently posted https://www.ambionix.com/blog/introducing-the-czp-1/ on here, and the audio engine in that was developed in that way.
It is true that it would be astounding to find if anyone has seen a LLM produce any useful generalization of anything resulting in a simplification. They seem to have a direct tendency to do the opposite. The brutal reality is humans have also undervalued this capability for a long time (I think the Poincare/Hilbert debate is relevant) to the point we are also taught that generalizations are, generally, bad and wrong.
This is also why they're so good at creating three.js or Blender work when the output is so easily constrained to "Look exactly like that". I recently posted https://www.ambionix.com/blog/introducing-the-czp-1/ on here, and the audio engine in that was developed in that way.
It is true that it would be astounding to find if anyone has seen a LLM produce any useful generalization of anything resulting in a simplification. They seem to have a direct tendency to do the opposite. The brutal reality is humans have also undervalued this capability for a long time (I think the Poincare/Hilbert debate is relevant) to the point we are also taught that generalizations are, generally, bad and wrong.