The authors explore the concept of intelligent behavior through lossless information compression, focusing on solving puzzles like those in the ARC-AGI challenge. Their method, CompressARC, achieves impressive results without pretraining or vast datasets, using only compression. They propose a future where tailored compressive objectives and efficient computation can extract deep intelligence from minimal input. Through a neural network framework, they demonstrate how compression correlates with accurate solutions. Their innovative approach navigates complex algorithms, information theory, and coding theories to create a practical system that decodes puzzle solutions efficiently. Their solution challenges conventional methods and offers a new perspective on artificial general intelligence.
https://iliao2345.github.io/blog_posts/arc_agi_without_pretraining/arc_agi_without_pretraining.html