# Visualizing computation in large-scale cellular automata

arXiv: https://arxiv.org/abs/2104.01008 · HTML: https://mlsanity.com/paper/2104.01008/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Evolving Structures in Complex Systems](https://mlsanity.com/paper/1911.01086/) (2019)
- [Local Causal States and Discrete Coherent Structures](https://mlsanity.com/paper/1801.00515/) (2018)

## Built on by (most cited first, 2 of 2)

- [Capturing Emerging Complexity in Lenia](https://mlsanity.com/paper/2305.09378/) (2023)
- [Neural Cellular Automata Learn General Features in their Hidden Channels](https://mlsanity.com/paper/2609.21870/) (2026)

## Abstract (arXiv)

Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution. Such a feat would require scaling up current simulation sizes to allow for enough computational capacity. Understanding complex computations happening in cellular automata and other systems capable of emergence poses many challenges, especially in large-scale systems. We propose methods for coarse-graining cellular automata based on frequency analysis of cell states, clustering and autoencoders. These innovative techniques facilitate the discovery of large-scale structure formation and complexity analysis in those systems. They emphasize interesting behaviors in elementary cellular automata while filtering out background patterns. Moreover, our methods reduce large 2D automata to smaller sizes and enable identifying systems that behave interestingly at multiple scales.