Comment by HarHarVeryFunny

> State grounding turns raw observations into objects, variables, and relations that can be tracked. Mechanism discovery finds how that state changes under an action and writes the rule as an executable program

The way I'm reading this isn't that they are writing a game simulator, but rather that they have two things they are evolving - a perceptual model of the game mapping from pixels to objects, and a behavioral model of how each action acts upon these perceptual objects. The behavioral model is written as a program that can be backtested by the game states and actions they have already taken to see if they are correctly predicting the resulting next game state.

The ARC AGI 3 games are non-trivial, and I think it's very impressive to see them doing well using this approach.

I'd agree with their conclusion:

> We read a saturated ARC‑3 as the new beginning: mechanism discovery as a general capability — grounding the causal structure of a world through the agentic loop of action and perception, in environments far richer than a 64×64 grid. This is where we are heading to.

This is the way that an animal learns about it's environment - by observation (and innate biases) to recognize the objects in the environment, and predict their behavior, both autonomous (which AGI ARC 3 doesn't test - the objects in the environment are passive), and in reaction to the animal's behavior. The animal predicts and observes, updating its predictions when it is wrong.

A system that could do this in a messy, dynamic, real-world environment would seem a like genuine step in the direction of animal intelligence, especially if it could ditch the symbolic representations.