posted in Technology

brianpeiris@brianpeiris@lemmy.ca
edited

ARC-AGI-3 news: In the past two weeks multiple groups have scored at least 99% on the public challenges at relatively low costs.

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.

arcprize.org/leaderboard/community
ARC PrizeARC Prize - Community LeaderboardCommunity submissions for the ARC-AGI benchmark.
brianpeiris@brianpeiris@lemmy.ca
edited

Replying to @⁨vermaterc@lemmy.ml⁩

ARC Prize maintains multiple tracks around their benchmarks. They have “verified” leaderboards, “community” leaderboards, and they also run the ARC Prize competition.

They update the “verified” leaderboards when they test raw LLMs without sophisticated harnesses. They seem to update this sporadically and only occasionally do press releases or blog posts about new scores. For example, the latest score from Claude Opus 5 (High) is 30% at $20,000, and they didn’t post about that as far as I know. Again, this just the raw LLM without an agentic or world-model harness.

The ARC Prize competition has a harder set of criteria. Participants have to use smaller, open models with a limited compute budget, with open source code, and of course the solutions are verified by ARC Prize at the end of the competition.

The “community” leaderboards, which is what this post is about, are self-reported and not verified by ARC Prize. There are no restrictions on what model is used or limitations on compute. So naturally they aren’t going to make official news releases about those, unless they decide to verify them at some point.

The only reason I chose to post this is that the top solutions seem legitimate, with source code released, and two of them have associated papers.