Building a Reasoning AI for the ARC Prize 2026

The author explores the challenge of building AI systems capable of solving novel problems, as opposed to just recognizing patterns. They describe their first attempt at the ARC Prize 2026 competition on Kaggle, which focuses on reasoning and generalization.

Details

The author recently joined the ARC-AGI-2 competition on Kaggle, which challenges participants to build AI systems capable of solving problems they've never seen before. Unlike typical machine learning tasks, ARC is not about training on massive datasets, but rather focuses on reasoning and generalization - something closer to human intelligence. For their first attempt, the author built a very simple baseline model that simply reads the input grid and generates an output grid filled with zeros. The goal was not to solve the problem, but to understand the dataset structure, learn the submission process, and make their first entry. The author notes that even advanced AI systems still struggle with ARC tasks, as they require understanding abstract patterns, applying transformations, and generalizing from very few examples. Unlike traditional machine learning, there is no training phase with millions of samples - each problem is a completely new challenge. The key insight is that ARC is fundamentally different from most AI challenges, as it's not about memorizing patterns, but rather about discovering rules, applying logic, and adapting to new problems.