  The Abstraction and Reasoning Corpus (ARC) is a challenging benchmark,
introduced to foster AI research towards human-level intelligence. It is a
collection of unique tasks about generating colored grids, specified by a few
examples only. In contrast to the transformation-based programs of existing
work, we introduce object-centric models that are in line with the natural
programs produced by humans. Our models can not only perform predictions, but
also provide joint descriptions for input/output pairs. The Minimum Description
Length (MDL) principle is used to efficiently search the large model space. A
diverse range of tasks are solved, and the learned models are similar to the
natural programs. We demonstrate the generality of our approach by applying it
to a different domain.
