# Online library learning in human visual puzzle solving

arXiv: https://arxiv.org/abs/2603.23244 · HTML: https://mlsanity.com/paper/2603.23244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [Hypothesis Search: Inductive Reasoning with Language Models](https://mlsanity.com/paper/2309.05660/) (2023)
- [Top-Down Synthesis for Library Learning](https://mlsanity.com/paper/2211.16605/) (2022)
- [Fast and flexible: Human program induction in abstract reasoning tasks](https://mlsanity.com/paper/2103.05823/) (2021)
- [Learning abstract structure for drawing by efficient motor program induction](https://mlsanity.com/paper/2008.03519/) (2020)
- [babble: Learning Better Abstractions with E-Graphs and Anti-Unification](https://mlsanity.com/paper/2212.04596/) (2022)
- [Program Synthesis with Pragmatic Communication](https://mlsanity.com/paper/2007.05060/) (2020)
- [Exploring the hierarchical structure of human plans via program generation](https://mlsanity.com/paper/2311.18644/) (2023)
- [Analysis of Language Change in Collaborative Instruction Following](https://mlsanity.com/paper/2109.04452/) (2021)
- [Cognitive maps are generative programs](https://mlsanity.com/paper/2504.20628/) (2025)
- [PACE: Procedural Abstractions for Communicating Efficiently](https://mlsanity.com/paper/2409.20120/) (2024)

## Built on by (most cited first, 1 of 1)

- [Prospective Compression in Human Abstraction Learning](https://mlsanity.com/paper/2605.09985/) (2026)

## Abstract (arXiv)

When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future. We study this process in a visual puzzle task where participants define and reuse helpers -- intermediate constructions that capture repeating structure. In an online experiment, participants solved puzzles of increasing difficulty. Early on, they created many helpers, favouring completeness over efficiency. With experience, helper use became more selective and efficient, reflecting sensitivity to reuse and cost. Access to helpers enabled participants to solve puzzles that were otherwise difficult or impossible. Computational modelling shows that human decision times and number of operations used to complete a puzzle increase with search space estimated by a program induction model with library learning. In contrast, raw program length predicts failure but not effort. Together, these results point to online library learning as a core mechanism in human problem solving, allowing people to flexibly build, refine, and reuse abstractions as task demands grow.