alt Hacker NewsThis is classic goalpost movement. Arc-AGI-3 was launched this year with roughly 0.5% success for frontier models. being able to 99% it in less than six months sets a new record for Arc-AGI saturation timeline. Speaking of singularity measures. It is definitely a big deal, not least in that Chollet needs to cancel his summer vacation and write Arc-AGI-4 now.
Replies
The point of Arc-AGI-3 is to measure model performance. We already know that models can one-shot and iterate on very rudimentary game implementations. And, naturally, once it effectively has a copy of the source code, it can use that to play the game better.
This harness is really moving the goalpost by defeating the entire point of the test. Instead of seeing the strength of a model's world view, its ability to internally derive and intuit rules, and its ability to keep track of game state over time, we're just letting the AI cheat. This is just the LLM equivalent of running a chess engine to the side.
And this harness would not work in a remotely complex game and relies on the fact that Arc-AGI-3 is a focused test that only made the games as complicated as they needed to be for current model performance.
Arc AGI are simple games, the hardness comes from the input being basically adversarial to LLM training. if you use an LLM scaffold that removes the adversarial part you are measuring something else.
the harness basically outsources the alien nature of what the LLM is asked to do to algorithms it writes. this would actually be impressive if you got it to do that for a much more complicated game than Arc.
with this harness the ARC AGI test becomes a test of whether or not the model can work out the transition rules in a very simple game.