PawsUp

I've been hearing about these for a few years now. Huge if they work out at a large scale.

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.

Context "Anewbis" by the Tesseract dev
CELPHASE did a better job.

cross-posted from: https://programming.dev/post/54810980

cross-posted from: https://lemmy.world/post/50445196

Note: I couldn't crosspost as the automod would remove it.