# Fast and flexible: Human program induction in abstract reasoning tasks

arXiv: https://arxiv.org/abs/2103.05823 · HTML: https://mlsanity.com/paper/2103.05823/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [Learning with Latent Language](https://mlsanity.com/paper/1711.00482/) (2017)
- [Human few-shot learning of compositional instructions](https://mlsanity.com/paper/1901.04587/) (2019)

## Built on by (most cited first, 30 of 33)

- [ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems](https://mlsanity.com/paper/2505.11831/) (2025)
- [Hypothesis Search: Inductive Reasoning with Language Models](https://mlsanity.com/paper/2309.05660/) (2023)
- [The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain](https://mlsanity.com/paper/2305.07141/) (2023)
- [Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement](https://mlsanity.com/paper/2310.08559/) (2023)
- [Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks](https://mlsanity.com/paper/2311.09247/) (2023)
- [Code Repair with LLMs gives an Exploration-Exploitation Tradeoff](https://mlsanity.com/paper/2405.17503/) (2024)
- [Communicating Natural Programs to Humans and Machines](https://mlsanity.com/paper/2106.07824/) (2021)
- [H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark](https://mlsanity.com/paper/2409.01374/) (2024)
- [Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2210.09880/) (2022)
- [Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2403.11793/) (2024)
- [CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay](https://mlsanity.com/paper/2402.04858/) (2024)
- [Abstract Visual Reasoning Enabled by Language](https://mlsanity.com/paper/2303.04091/) (2023)
- [LoopViT: Scaling Visual ARC with Looped Transformers](https://mlsanity.com/paper/2602.02156/) (2026)
- [ANPL: Towards Natural Programming with Interactive Decomposition](https://mlsanity.com/paper/2305.18498/) (2023)
- [Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer](https://mlsanity.com/paper/2306.08204/) (2023)
- [A Neurodiversity-Inspired Solver for the Abstraction & Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis](https://mlsanity.com/paper/2302.09425/) (2023)
- [Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method](https://mlsanity.com/paper/2311.08083/) (2023)
- [A Divide-Align-Conquer Strategy for Program Synthesis](https://mlsanity.com/paper/2301.03094/) (2023)
- [Tackling the Abstraction and Reasoning Corpus (ARC) with Object-centric Models and the MDL Principle](https://mlsanity.com/paper/2311.00545/) (2023)
- [Do Large Language Models Solve ARC Visual Analogies Like People Do?](https://mlsanity.com/paper/2403.09734/) (2024)
- [ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning](https://mlsanity.com/paper/2407.20806/) (2024)
- [Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning](https://mlsanity.com/paper/2204.01437/) (2022)
- [Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2405.06399/) (2024)
- [Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game](https://mlsanity.com/paper/2406.11012/) (2024)
- [Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL](https://mlsanity.com/paper/2408.14855/) (2024)
- [NSA: Neuro-symbolic ARC Challenge](https://mlsanity.com/paper/2501.04424/) (2025)
- [From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark](https://mlsanity.com/paper/2505.17482/) (2025)
- [Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task](https://mlsanity.com/paper/2410.11324/) (2024)
- [Vector Symbolic Algebras for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2511.08747/) (2025)
- [Online library learning in human visual puzzle solving](https://mlsanity.com/paper/2603.23244/) (2026)

## Abstract (arXiv)

The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collected from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). Although this subset of tasks contains considerable variation, our results showed that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 80% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability within the action sequences during the generation process, the natural language descriptions to describe the transformations for each task, and the errors people made. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution. Future modeling work could incorporate these findings, potentially by connecting the natural language descriptions we collected here to the underlying semantics of ARC.