将抽象推理任务视为图像到图像的转换问题，用视觉模型取得新突破。
ARC Is a Vision Problem!
- 把ARC任务看作图像到图像的映射，用视觉模型直接处理输入画面
- 在ARC-1上达到60.4%准确率，远超其他从零训练的非语言模型
- 适合关注视觉推理、跨模态智能的研究者和应用开发者
抽象与推理语料库（ARC）旨在推动抽象推理研究，这是人类智能的核心。现有方法通常将其视为语言任务，依赖大语言模型或循环推理模型。然而，尽管ARC中的题目本质上是视觉的，现有研究极少从视觉角度切入。本文提出一种视觉范式，将ARC建模为图像到图像的翻译问题。通过在“画布”上表示输入，使其可像自然图像一样被处理，进而使用标准视觉架构（如基础视觉变换器，ViT）完成图像映射。模型仅在ARC数据上从零训练，通过测试时训练泛化至未见任务。所提框架名为视觉ARC（VARC），在ARC-1基准上达到60.4%准确率，显著优于其他从零训练的方法，结果与领先的大语言模型相当，并接近人类平均表现。
原文摘要 · Abstract (English)
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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