The top three solutions come from independent researchers. The best solution was built by a group of PhDs and professors, who released a corresponding paper. They all make use of some form of world-model.
I’ve generally been a skeptic, and I still am, but this news surprised me because I expected ARC-AGI-3 to remain difficult for a long while.
Note that the scores are self-reported and need to be independently verified. The solutions have not been tested against the larger private test set.
Primer on ARC-AGI-3:
ARC-AGI-3 is an interactive reasoning benchmark which challenges AI agents to explore novel environments, acquire goals on the fly, build adaptable world models, and learn continuously.
A 100% score means AI agents can beat every game as efficiently as humans.
Instead of solving static puzzles, agents must learn from experience inside each environment—perceiving what matters, selecting actions, and adapting their strategy without relying on natural-language instructions.
It think it’s still unwise to talk about these topics in broad terms like AGI and even “intelligence”. We still have to pick the capabilities apart to have useful discussions about them. I agree these games are better tests than many benchmarks, but it’s also important to note that these solutions use a combination of well-designed deterministic harnesses, as well as LLMs. So it’s inaccurate to say that “LLMs have achieved AGI” (not sure if that’s what you were getting at). This feels like an important milestone, but we’ll have to continue to probe for failure cases in other categories of problems.
Aside from emotional intelligence, experience, embodiment, etc., these ARC-AGI-3 solutions all rely on the sandbox being a safe environment to fail. They iterate through the problem thousands of times before coming to a final solution. Many real-world human problems cannot be re-tried safely or efficiently.