What I've noticed when testing previous versions of Grok, on paper they were better at benchmarks, but when I used it the responses were always worse than Sonnet and Gemini even though Grok had higher benchmark scores.
Occasionally I test Grok to see if it could become my daily driver but it's never produced better answers than Claude or Gemini for me, regardless of what their marketing shows.
That's kind of the idea behind ARC-AGI. Training on available ARC benchmarks does not generalize. Unless it does... in which case, mission accomplished.
They have walked back the initial notion that success on the test requires, or demonstrates, the emergence of AGI. But the general idea remains, which is that no amount of pretraining on the publicly-available problems will help solve the specific problems in the (theoretically-undisclosed) test set unless the model is exhibiting genuine human-like intelligence.
Getting almost 16% on ARC-AGI-2 is pretty interesting. I wish somebody else had done it, though.
This is not hard to build datasets that have these types of problems in them, and I would expect LLMs to generalize this well. I don’t see how this is any different really than any other type of problem LLMs are good at given they have the dataset to study.
I get they keep the test updated with secret problems, but I don’t see how companies can’t game this just by investing in building their own datasets, even if it means paying teams of smart people to generate them.
But the lack of a CLI tool like codex, claude code or gemini-cli is preventing it from being a daily driver. Launching a browser and having to manually upload repomixed content is just blech.
With gemini I can just go `gemini -p "@repomix-output.xml review this code..."`