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The Abstraction and Reasoning Corpus (ARC) is a challenging benchmark,introduced to foster AI research towards human-level intelligence. It is acollection of unique tasks about generating colored grids, specified by a fewexamples only. In contrast to the transformation-based programs of existingwork, we introduce object-centric models that are in line with the naturalprograms produced by humans. Our models can not only perform predictions, butalso provide joint descriptions for input/output pairs. The Minimum DescriptionLength (MDL) principle is used to efficiently search the large model space. Adiverse range of tasks are solved, and the learned models are similar to thenatural programs. We demonstrate the generality of our approach by applying itto a different domain.

Tackling the Abstraction and Reasoning Corpus (ARC) with Object-centric Models and the MDL Principle