That's a stretch. This is a problem at which LLMs are bad. That does not imply it's a good measure of artificial general intelligence.

After working a few of the problems, I was wondering how many different transformation rules the problem generator has. Not very many, it seems. So the problem breaks down into extracting the set of transformation rules from the data, then applying them to new problems. The first part of that is hard. It's a feature extraction problem. The transformations seem to be applied rigidly, so once you have the transformation rules, and have selected the ones that work for all the input cases, application should be straightforward.

This seems to need explicit feature extraction, rather than the combined feature extraction and exploitation LLMs use. Has anyone extracted the rule set from the test cases yet?