I've also worked extensively on ARC AGI 1/2, and I mainly agree. Marketing and training. Performance of LLMs on ARC is most importantly a function of training on grid/table-like data. It doesn't have to be specifically synthetic ARC data though. Training an LLM to be better at perceiving grid-like arrangements of data in a spatial way like an image, rather than just tabular, is hugely useful for things outside of ARC benchmarks, though it's a narrow skill. Hence, I'm sure they do it. I want them to do that. I believe the labs when they say they didn't train specifically for ARC-AGI 1/2 (where did Google say otherwise? I don't see it). But it does not mean the models are getting better at general purpose reasoning. They were already plenty good enough at that. You can describe ARC images in words and reason about it using a level of intelligence LLMs have had for years: they're designed to be easy! LLMs just couldn't reason about image-like grids very well.

ARC-AGI isn't perfect, but it helps demonstrates the gap. I'm sure all companies optimize their models for this benchmark given its dominance.

Why do you think DeepSeek isn't also fine tuned on ARC AGI? Maybe they're more fine tuned on ARC AGI but still get worse scores. There's no way to know.