Using Large Language Models do solve the Abstraction and Reasoning Challenge

Current deep learning models, while adept at specific tasks, often struggle with human-like adaptability to new and varied challenges. This research delves into the creation of artificial intelligence systems that can mimic the generalization capabilities of human intelligence, particularly through the use of the Abstraction and Reasoning Corpus (ARC). ARC is a compilation of reasoning tasks that are deeply rooted in Knowledge Priors, which are essential human skills for effective problem-solving, such as counting. The proposed solution involves integrating a Large Language Model (LLM) with several DreamCoders, forming a Mixture of Experts (MoE) framework. In this framework, the LLM acts as a classifier, pinpointing the specific skills required for each ARC task. Following this identification, the problem is delegated to a specialized DreamCoder, each trained solely to tackle tasks within the identified skill set.