I think this is a bad way to look at it. LLMs can probably conceive of most things that are representable within the embedding space. Ordinarily in mathemati

I think this is a bad way to look at it. LLMs can probably conceive of most things that are representable within the embedding space. Ordinarily in mathematics there’s a TON of papers to write just combining low level problems with different techniques. Better still, and often considered groundbreaking is borrowing techniques from other fields and adapting them or creating analogous methods to solve problems. A lot of landmark papers have been written this way. This is also what transformers are sort of good at within other contexts. They have super human breadth so I’m hopeful they’ll become real assets in math for a long time. Though the leaps necessary to adapt a technique in a nonobvious way might be too much for a while longer. We’ll see. Truly novel techniques are quite rate indeed and I don’t know if LLMs can represent them faithfully in their embedding space or not. My inclination is that they probably can most of the time, but I don’t know. Mathematicians would describe such thins as “alien”.