用最小描述长度原理实现高效可解释的程序合成。
MADIL: An MDL-based Framework for Efficient Program Synthesis in the ARC Benchmark
- 基于最小描述长度原则进行模式分解，实现结构化泛化。
- 在ARC基准上达到7%准确率，显著降低计算成本。
- 适合关注效率与可解释性的模型研究者。
人工智能在特定任务上已取得显著进展，但在高效技能获取和泛化方面仍面临挑战。阿布拉斯与推理语料库（ARC）基准通过极低训练需求评估智能水平。尽管大语言模型（LLMs）近期提升了ARC表现，但依赖大量预训练和高计算开销。本文提出一种基于最小描述长度（MDL）原则的新方法MADIL（MDL-based AI），实现高效的归纳学习。MADIL通过模式分解实现结构化泛化，在ArcPrize 2024中取得7%的准确率，虽低于基于LLM的方法，但具备更高效率与可解释性。论文详述了MADIL方法、在ARC上的应用及实验评估。
原文摘要 · Abstract (English)
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.
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