# MADIL: An MDL-based Framework for Efficient Program Synthesis in the ARC Benchmark

arXiv: https://arxiv.org/abs/2505.01081 · HTML: https://mlsanity.com/paper/2505.01081/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2025.

## Built on by (most cited first, 3 of 3)

- [The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning](https://mlsanity.com/paper/2603.13372/) (2026)
- [ARC-AGI Without Pretraining](https://mlsanity.com/paper/2512.06104/) (2025)
- [ARCTraj: A Dataset and Benchmark of Human Reasoning Trajectories for Abstract Problem Solving](https://mlsanity.com/paper/2511.11079/) (2025)

## Abstract (arXiv)

Artificial Intelligence (AI) has achieved remarkable success in specialized tasks but struggles with efficient skill acquisition and generalization. The Abstraction and Reasoning Corpus (ARC) benchmark evaluates intelligence based on minimal training requirements. While Large Language Models (LLMs) have recently improved ARC performance, they rely on extensive pre-training and high computational costs. We introduce MADIL (MDL-based AI), a novel approach leveraging the Minimum Description Length (MDL) principle for efficient inductive learning. MADIL performs pattern-based decomposition, enabling structured generalization. While its performance (7% at ArcPrize 2024) remains below LLM-based methods, it offers greater efficiency and interpretability. This paper details MADIL's methodology, its application to ARC, and experimental evaluations.