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We attempt to solve the Abstraction and Reasoning Corpus (ARC) Challengeusing Large Language Models (LLMs) as a system of multiple expert agents. Usingthe flexibility of LLMs to be prompted to do various novel tasks usingzero-shot, few-shot, context-grounded prompting, we explore the feasibility ofusing LLMs to solve the ARC Challenge. We firstly convert the input image intomultiple suitable text-based abstraction spaces. We then utilise theassociative power of LLMs to derive the input-output relationship and map thisto actions in the form of a working program, similar to Voyager / Ghost in theMineCraft. In addition, we use iterative environmental feedback in order toguide LLMs to solve the task. Our proposed approach achieves 50 solves out of111 training set problems (45%) with just three abstraction spaces - grid,object and pixel - and we believe that with more abstraction spaces andlearnable actions, we will be able to solve more.

Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge