I'm also waiting for my answer on the role of the Gemini formalizer, but reading between the lines, it looks like it was only used during training the "solver network", but not used in solving the IMO problems. If so then the hyping is greatly premature, as the hybrid formalizer/solver is the whole novelty of this, but it's not good enough to use end-to-end?
You cannot say AlphaProof learned enough to solve problems if formalization made them easier to solve in the first place! You can say that the "solver network" part learned enough to solve formalized problems better than prior training methods.
Both changes were trivial: it had one incorrect (but unnecessary) import, and it used the syntax from Lean 3 instead of Lean 4 in one lambda definition. A system that was trained harder on Lean would not make those mistakes.
The one actual error it made was in not proposing that the other direction of the "if and only if" is required. Again, I am quite confident that this formalisation failure mode is not hard to solve in a system that is, like, actually trained to do this.
Obviously formalising problems that a working mathematicican solves is dramatically harder than formalising IMO problems, and is presumably way ahead of the state of the art.
Why?
IDK, even for translation between languages outside of mathematics, missing small qualifiers that change the whole meaning of the sentence is a worrying failure mode I've seen, and with mathematical problems there are a lot more cases like that.
> While the problem statements were formalized into Lean by hand, the answers within the problem statements were generated and formalized by the agent.
However, it's unclear what initial format was given to the agents that allowed this step