RE-ARC: Reverse-Engineering the Abstraction and Reasoning Corpus

Addressing ARC via Procedural Example Generation

This repository presents code to procedurally generate examples for the ARC training tasks. For each of the 400 tasks, an example generator is provided. See the demo notebook for example usage of the code and a visualization of the data. The primary entry point is the generate_dataset function defined in main.py. The file re_arc.zip contains 1000 verified generated examples for each of the 400 training tasks (re_arc/tasks contains a json file for each ARC task containing an array of json objects each with keys "input" and "output") alongside two difficulty metrics for each example and some task-level metadata about runtime and sample-efficiency, the result of running the notebook, which calls generate_dataset with the default parameter values. The only major dependency is the ARC-DSL, which is however included as a single file in dsl.py, as it is not provided as a Python package. Other relevant files are generators.py, which contains the task-specific example generators, and verifiers.py, which contains the corresponding task solver programs used for keeping only generated examples that are valid.

For a more in-depth description of the work, see the notes on arxiv.

Example usage:

from main import demo_generator
demo_generator('00d62c1b')

00d62c1b (original)

00d62c1b (generated)