From the appendix [0]: "The proofs written by agents are just as “good” as any other proof of the same theorem, in that they prove the equivalence theorem. However, it remains to be seen whether agents can scale this verbose proof style directly to more complex programs. Experience from other agent-based verification projects, such as MachCSL, suggests that the answer might be no: the agents hit a complexity wall with the verbose proof style, and scaling up requires the proofs to be structured around better modular abstractions and reasoning principles. If that is the case, how can we help agents discover and introduce the right abstractions to reduce this complexity? Does this require input from expert proof engineers, or can we extract common principles that can guide agents to discovering good abstractions that will enable scalable proofs?"

Some mathematicians have observed a similar pattern in the recent LLM proof drops, leading them to suggest that LLMs will hit a wall in mathematical capability just beyond the convex hull of human mathematics. The sort of proofs done in formal verification are a pretty interesting litmus test for LLM reasoning since the systems considered aren't necessarily on distribution (compare with math where the "systems" and structures worked with, and proofs about them, make appearances in thousands of individual books) so the advantages in broad domain knowledge pretrained LLMs have over humans doesn't really buy you much. IMO this observation from Anthropic/Theorem lends more credibility to the convex hull hypothesis.

[0] https://www-cdn.anthropic.com/files/4zrzovbb/website/6867ead...