The ARC-AGI benchmark, short for Abstraction and Reasoning Corpus for Artificial General Intelligence , has become an important tool for testing how “smart” artificial systems really are. Most AI tests focus on repetition and memorization, but ARC-AGI is built to measure reasoning and generalization. It asks a simple but deep question: can a system learn a pattern from a few examples and then apply that pattern to a completely new problem? The answer to that question touches both computer science and philosophy. It raises another, broader question: what does it mean to be intelligent? What Is ARC-AGI? ARC-AGI was created by computer scientist François Chollet to see whether machines can learn new skills the way humans do. Each problem is a small grid puzzle. The system is given a few examples of how an input grid changes into an output grid. Then it has to find the hidden rule that connects them and apply it to a new grid. There are no large datasets or tricks involved. The system ...