[ndriscoll]

This is literally the same thing as having the model write well factored, readable code. You can tell it to do things like avoid mixing abstraction levels within a function/proof, create interfaces (definitions/axioms) for useful ideas, etc. You can also work with it interactively (this is how I work with programming), so you can ask it to factor things in the way you prefer on the fly.

[integralid]

>This is literally the same thing as

No.

>You can

Not right now, right? I don't think current AI automated proofs are smart enough to introduce nontrivial abstractions.

Anyway I think you're missing the point of parent's posts. Math is not proofs. Back then some time ago four color theorem "proof" was very controversial, because it was a computer assisted exhaustive check of every possibility, impossible to verify by a human. It didn't bring any insight.

In general, on some level, proofs like not that important for mathematicians. I mean, for example, Riemann hypothesis or P?=NP proofs would be groundbreaking not because anyone has doubts that P=NP, but because we expect the proofs will be enlightening and will use some novel technique

[ndriscoll]

Right, in the same way that programs are not opcodes. They're written to be read and understood by people. Language models can deal with this.

I'm not sure what your threshold for "trivial" is (e.g. would inventing groups from nothing be trivial? Would figuring out what various definitions in condensed mathematics "must be" to establish a correspondence with existing theory be trivial?), but I see LLMs come up with their own reasonable abstractions/interfaces just fine.