Math is an interesting case. Arguably a lot of the groundwork was already there. There’s a huge body of academic literature that’s historically been difficult for humans to draw on for various reasons. It’s relatively easier for LLMs to mine all of that for relevant prior work that it can recycle into proof segments to try. And we have theorem proving software that it can use to try out these proof segments and validate them. That creates a tight, self-contained, and relatively inexpensive feedback loop that lends itself well to a host of agents grinding away, as long as you have budget to pay for all that LLM inference.

I’m not sure the same conditions exist in other fields that deal with real world phenomena. Experiments to test hypotheses about biology or physics can take years and cost millions of dollars to run. They rarely scale down to the millipennies and milliseconds scale that makes letting a swarm of agents throw a bunch of shit at the wall and see what sticks a feasible option for finding mathematical proofs.