At the very first glance, it's like ARC 1 with some structures serving as contextual data, and more complicated symmetries / topological transformations.
Now, I wonder what surprises are to be found in the full dataset.
The focus on solving cost efficiently discrete tasks might actually lead us towards deep learning systems that could be used reliably in production, and not just give a whoa effect or need to be constantly supervised