ARC-AGI-2 Technical Report

TL;DR

This paper introduces a transformer-based system for the ARC challenge that combines neural inference, structure-aware priors, and online adaptation, significantly improving generalization beyond pattern matching.

Contribution

It presents a novel approach integrating sequence modeling, symmetry-based augmentation, test-time training, and symmetry-aware decoding to enhance ARC problem-solving capabilities.

Findings

Achieved significant performance improvement over baseline transformers.

Surpassed previous neural ARC solvers in generalization.

Close to human-level performance on ARC tasks.

Abstract

The Abstraction and Reasoning Corpus (ARC) is designed to assess generalization beyond pattern matching, requiring models to infer symbolic rules from very few examples. In this work, we present a transformer-based system that advances ARC performance by combining neural inference with structure-aware priors and online task adaptation. Our approach is built on four key ideas. First, we reformulate ARC reasoning as a sequence modeling problem using a compact task encoding with only 125 tokens, enabling efficient long-context processing with a modified LongT5 architecture. Second, we introduce a principled augmentation framework based on group symmetries, grid traversals, and automata perturbations, enforcing invariance to representation changes. Third, we apply test-time training (TTT) with lightweight LoRA adaptation, allowing the model to specialize to each unseen task by learning its transformation logic from demonstrations. Fourth, we design a symmetry-aware decoding and scoring pipeline that aggregates likelihoods across augmented task views, effectively performing ``multi-perspective reasoning'' over candidate solutions. We demonstrate that these components work synergistically: augmentations expand hypothesis space, TTT sharpens local reasoning, and symmetry-based scoring improves solution consistency. Our final system achieves a significant improvement over transformer baselines and surpasses prior neural ARC solvers, closing the gap toward human-level generalization.