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Notes -
Indeed, there's almost nothing scientific about the scoring system of ARC-AGI-3; the test itself is kinda neat, and still highlights something that smart humans do (somewhat) better than the best LLMs, but it's dropped any pretense at being an actual measure of "general intelligence", and frankly they deserve to be ridiculed for the sensationalist scores.
Why is completion speed the main factor? Why is the difference squared? Speedruns are not how we define intelligence. If the squirrel in your backyard can solve sudokus, but a top-10th-percentile-of-self-selected-sudoku-solvers human can do it faster, you don't laugh and say "ha ha, this squirrel is so dumb". Also note that the test cuts the model off if it takes 5x longer than the smart human, and later questions build on earlier ones, so if a model goes slowly once it's handicapped for the rest of the test. (Again, this is probably completely intentional, to help deflate scores further.) They used a majority-of-self-selected-humans-can-solve-this metric for puzzle inclusion but not for the scoring. Why? Pure showmanship.
I suspect that average humans who take the test would probably also get a very low score! The old tests and metrics (including ARC-AGI-2) were useful because they showed something that humans genuinely find easy, but LLMs fail at. Those metrics have almost reached saturation, so I guess now we're switching to puzzles that some humans can solve but LLMs ... uh ... solve a bit slower. Ok?
But hey, the "0.5%" number does help low-information AI skeptics like OP point and laugh, so it's another "win" for AI journalism.
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