While Artificial Intelligence (AI) models have achieved human or even superhuman performance in narrowly defined applications, they still struggle to show signs of broader and more flexible intelligence. The Abstraction and Reasoning Corpus (ARC), introduced by Fran\c{c}ois Chollet, aims to assess how close AI systems are to human-like cognitive abilities. Most current approaches rely on carefully handcrafted domain-specific languages (DSLs), which are used to brute-force solutions to the tasks present in ARC. In this work, we propose a general framework for solving ARC based on natural language descriptions of the tasks. While not yet beating state-of-the-art DSL models on ARC, we demonstrate the immense potential of our approach hinted at by the ability to solve previously unsolved tasks.

翻译：尽管人工智能（AI）模型在狭义定义的应用中已达到人类甚至超人类的性能，但在展现更广泛、更灵活的智能迹象方面仍面临挑战。由François Chollet提出的抽象与推理语料库（ARC）旨在评估AI系统与人类认知能力的接近程度。当前大多数方法依赖于精心设计的领域专用语言（DSL），通过暴力搜索方式解决ARC中的任务。本文提出了一种基于任务自然语言描述的通用ARC求解框架。尽管该框架在ARC上尚未超越最先进的DSL模型，但通过其解决此前未解任务的能力，充分展示了该方法的巨大潜力。