You really should play the 25 games before stating that it's "simple". The benchmark doesn't just track "completion", it also tracks the number of steps, and the score is based on the median steps took by human players. So in order to get 99% it would mean that the model solved every level of every game in less steps than the median humans. Which, having played the games and having setup harnesses for local models, I find hard to believe.

Also the models have to figure out what "end" means. And each game involves some kind of "gotchas" thrown in the harder levels. Some games are only solved by about 2/10 people trying them.

The 99% result most likely has some leakage somewhere, either in the preparation of the environments, or from session to session.

Seriously, play some of the games. They're fun.

You are not given the rules or the winning conditions. You are only given a potentially windowed and/or degenerate visualizer of the underlying game state along with the UI and told to just figure it out. And you as a human will, in a couple moves. An LLM? Not so much. But they do eventually solve them. And given enough moves, victory is inevitable, but you are penalized for taking more moves than a human, yet also slightly punished if you find a better solution by capping your reward to 115%.

Not my sniff test :)

> # FRAMEWORK ARTEFACT: the run's very first transition is replayed WITHOUT advancing state # (tools.py:954 and agent.py:468 both `continue` before `state = next_state`). So on the # level that contains that step (level 0) our counters start exactly one action behind. # That skipped step was action 1 with BOTH avatars moving, so seeding n=1, bumps=0 reproduces # the framework's lagged state exactly. # CAVEAT: this seed is only right while level 0 has never been RESET. If you ever RESET # level 0, change the seed to n=0 (after a reset the rollout re-inits and no longer skips).

from here - https://huggingface.co/datasets/schema-harness/arc-agi-3-sch...

That tells me that there is some leakage between runs. The idea of ARC3 is that agents start working blind, on new tasks, via API. A RESET is counted as one action. Without seeing the actual code that produced these traces we have no way of knowing how many iterations it took, if the "framework" played the same level multiple times (comment hint above makes it likely) and so on. That's why I said that before we actually see the code / can replicate / ARC team confirms it on new envs, this should be taken with a grain of salt.