ARC Is a Vision Problem!

• URL: http://arxiv.org/abs/2511.14761v1
• Date: Tue, 18 Nov 2025 18:59:49 GMT
• Title: ARC Is a Vision Problem!
• Authors: Keya Hu, Ali Cy, Linlu Qiu, Xiaoman Delores Ding, Runqian Wang, Yeyin Eva Zhu, Jacob Andreas, Kaiming He,
• Abstract summary: We formulate ARC within a vision paradigm, framing it as an image-to-image translation problem.<n>Our framework, termed Vision ARC, achieves 60.4% accuracy on the ARC-1 benchmark.
• Score: 50.59206008530851
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a language-oriented problem, addressed by large language models (LLMs) or recurrent reasoning models. However, although the puzzle-like tasks in ARC are inherently visual, existing research has rarely approached the problem from a vision-centric perspective. In this work, we formulate ARC within a vision paradigm, framing it as an image-to-image translation problem. To incorporate visual priors, we represent the inputs on a "canvas" that can be processed like natural images. It is then natural for us to apply standard vision architectures, such as a vanilla Vision Transformer (ViT), to perform image-to-image mapping. Our model is trained from scratch solely on ARC data and generalizes to unseen tasks through test-time training. Our framework, termed Vision ARC (VARC), achieves 60.4% accuracy on the ARC-1 benchmark, substantially outperforming existing methods that are also trained from scratch. Our results are competitive with those of leading LLMs and close the gap to average human performance.

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