Interesting! I remember that ChatGPT-3.5-Turbo could, surprisingly, play very long games without any problems. Although it wasn't very good, it almost never hallucinated moves. e.g  https://lichess.org/tKa7ObGI

Most benchmarks face inherent problem of goodhart's law: as soon as they become a target metric, efforts converge on optimizing for the benchmark itself, potentially diverging from the capabilities it was meant to measure.

The methods used to solve these "new" problems are, as I've already stated, highly amenable to brute-force approaches. It's more of a computation problem [BitterLesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html). I'm again not surprised these kinds of problems got solved.

While these problems may be new, they employ very similar methods to those definitely used in previous competitive programming and Math Olympiad problems. I don't think the author has really looked into the specifics of how AlphaGeometry and AlphaProof have come up with these solutions. It's honestly disappointing to see that they were able to mislead such people (if he truly hasn't looked into the specifics). It seems more like he wants to use his status to push a particular narrative.

I would bet a lot that this system will fail on almost any combinatorics problem at the moment...

Since this is a point that seemingly causes a lot of confusion and misunderstanding, I'll try to find some time to write down my reasoning and thoughts in a more exhaustive post.

Honestly, I think people are overestimating this.  Some quick thoughts:

They already claimed once to be at a 1200 Elo level in competitive programming on the Codeforces, but in real competition settings, it only reached a level of, as I remember correctly, around ~500 as people found the corresponding account they used for testing. Also, their solution to one very hard problem was exactly the same as one from a human competitor, and they likely had this exact problem already in their training data, so their training data was likely contaminated.

I'm not really surprised that this type of algorithm will perform well on ad-hoc & constructive type problems. Generally, in a lot of cases, you differentiate the problem into small cases, find a pattern (mathematical model), and generalize from it. These kinds of math olympiad problems are quite similar in nature. So current models have a very good sense of what a "pattern" is and, of course, have many more FLOPS to prove their hypotheses in small cases. Personally, I find solving ad-hoc problems pretty boring, because mostly you're taking in some patterns as hypotheses and just brute-forcing your way through to see what works and what doesn't -> machines can do that faster.

So I'm not really sure they can solve their definition of "creativity" by adding more and more additional compute since all the Alpha-style algorithms (RL in AlphaZero, AlphaGo, etc.) are currently trying to make finding those heuristics as good as possible while trying to reduce branching in order to save compute. I don't know how far you can go with throwing more and more compute at it and brute-forcing approaches with certain heuristics until finding a solution to the problem.

summing up these are similar methods used by researchers decades ago, but we have a lot more FLOPS now.

The LLM's only use was to translate problems to a formal language, in this case Lean, in order to generate training data for their RL algorithm used in all of the Alpha-type algos.

That's also why I think they started with these types of problems, because you can approximate the compute needed. The generative part involved in geometry, ad-hoc, etc., is much less than in algebra, combinatorics, etc., since it's much more mechanical in nature. You will be able to solve a lot in these types of problems with just scaling compute.

AlphaProof seems to be augmented with an LLM trained on symbolic chains so from statement -> examples -> conjecture -> proof/disproof of conjecture, maybe even multiple conjecture nodes. So on the other hand, within natural language, which can come up with conjectures from a next-token point of perspective, could be working.

The system could comprise a "generator" model creating exhaustive examples from the statement, an "observer" model studying these examples to find and intersect their properties, and AlphaProof as the "prover" to prove or disprove conjectures. Whether using LLMs or traditional models is up for experimentation, but each component must be formally verified.

I think you can see that at work in the solution of problem 2, which is correct but feels very unintuitive, and there are much more elegant solutions to this problem. (I specifically only looked at this one for now, maybe will update in the edit for the other problems)