# Finding structure in logographic writing with library learning

arXiv: https://arxiv.org/abs/2405.06906 · HTML: https://mlsanity.com/paper/2405.06906/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 3 of 3)

- [Compound Probabilistic Context-Free Grammars for Grammar Induction](https://mlsanity.com/paper/1906.10225/) (2019)
- [Top-Down Synthesis for Library Learning](https://mlsanity.com/paper/2211.16605/) (2022)
- [Identifying concept libraries from language about object structure](https://mlsanity.com/paper/2205.05666/) (2022)

## Built on by (most cited first, 5 of 5)

- [LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP](https://mlsanity.com/paper/2408.04628/) (2024)
- [Visual Theory of Mind Enables the Invention of Proto-Writing](https://mlsanity.com/paper/2502.01568/) (2025)
- [Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors](https://mlsanity.com/paper/2601.05508/) (2026)
- [Rational Communication Shapes Morphological Composition](https://mlsanity.com/paper/2605.03510/) (2026)
- [Hypothesis-guided discovery of cognitive algorithms via program refinement](https://mlsanity.com/paper/2610.02523/) (2026)

## Abstract (arXiv)

One hallmark of human language is its combinatoriality -- reusing a relatively small inventory of building blocks to create a far larger inventory of increasingly complex structures. In this paper, we explore the idea that combinatoriality in language reflects a human inductive bias toward representational efficiency in symbol systems. We develop a computational framework for discovering structure in a writing system. Built on top of state-of-the-art library learning and program synthesis techniques, our computational framework discovers known linguistic structures in the Chinese writing system and reveals how the system evolves towards simplification under pressures for representational efficiency. We demonstrate how a library learning approach, utilizing learned abstractions and compression, may help reveal the fundamental computational principles that underlie the creation of combinatorial structures in human cognition, and offer broader insights into the evolution of efficient communication systems.