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

arXiv: https://arxiv.org/abs/2310.05146 · HTML: https://mlsanity.com/paper/2310.05146/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 17 of its 19 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 17 of 17)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory](https://mlsanity.com/paper/2305.17144/) (2023)
- [Large Language Models as General Pattern Machines](https://mlsanity.com/paper/2307.04721/) (2023)
- [LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations](https://mlsanity.com/paper/2305.18354/) (2023)
- [Communicating Natural Programs to Humans and Machines](https://mlsanity.com/paper/2106.07824/) (2021)
- [Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2210.09880/) (2022)
- [Abstract Visual Reasoning Enabled by Language](https://mlsanity.com/paper/2303.04091/) (2023)
- [First Steps of an Approach to the ARC Challenge based on Descriptive Grid Models and the Minimum Description Length Principle](https://mlsanity.com/paper/2112.00848/) (2021)
- [Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer](https://mlsanity.com/paper/2306.08204/) (2023)
- [An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge](https://mlsanity.com/paper/2306.03553/) (2023)

## Built on by (most cited first, 11 of 11)

- [Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples](https://mlsanity.com/paper/2406.05673/) (2024)
- [Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark](https://mlsanity.com/paper/2410.14702/) (2024)
- [IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web](https://mlsanity.com/paper/2409.18980/) (2024)
- [HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis](https://mlsanity.com/paper/2405.15880/) (2024)
- [The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding](https://mlsanity.com/paper/2502.08946/) (2025)
- [ARC-GEN: A Mimetic Procedural Benchmark Generator for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2511.00162/) (2025)
- [From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems](https://mlsanity.com/paper/2506.04565/) (2025)
- [Understanding LLMs' Fluid Intelligence Deficiency: An Analysis of the ARC Task](https://mlsanity.com/paper/2502.07190/) (2025)
- [AbstractBeam: Enhancing Bottom-Up Program Synthesis using Library Learning](https://mlsanity.com/paper/2405.17514/) (2024)
- [Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL](https://mlsanity.com/paper/2408.14855/) (2024)
- [Solomonoff-Inspired Hypothesis Ranking with LLMs for Prediction Under Uncertainty](https://mlsanity.com/paper/2512.17145/) (2025)

## Abstract (arXiv)

We attempt to solve the Abstraction and Reasoning Corpus (ARC) Challenge using Large Language Models (LLMs) as a system of multiple expert agents. Using the flexibility of LLMs to be prompted to do various novel tasks using zero-shot, few-shot, context-grounded prompting, we explore the feasibility of using LLMs to solve the ARC Challenge. We firstly convert the input image into multiple suitable text-based abstraction spaces. We then utilise the associative power of LLMs to derive the input-output relationship and map this to actions in the form of a working program, similar to Voyager / Ghost in the MineCraft. In addition, we use iterative environmental feedback in order to guide LLMs to solve the task. Our proposed approach achieves 50 solves out of 111 training set problems (45%) with just three abstraction spaces - grid, object and pixel - and we believe that with more abstraction spaces and learnable actions, we will be able to solve more.