@inproceedings{mittal-etal-2026-textnca,
title = "{T}ext{NCA}: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention",
author = "Mittal, Avni and
Anand, Avinash and
Kumar, Ashutosh and
Kukreja, Dikshant and
Prasad, Kritarth and
Dulloo, Sushane and
Cambria, Erik and
Liu, Timothy and
Wang, Zhengkui and
Shah, Rajiv Ratn",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1413/",
pages = "30116--30141",
ISBN = "979-8-89176-462-0",
abstract = "Neural Cellular Automata (NCAs) generate complex spatial patterns in vision by repeatedly applying a shared local rule to persistent cell states, combining local perception, weight-shared iteration, and gated in-place updates. We test this principle in language modelling with TextNCA, a 1D causal model in which token positions act as cells, windowed attention provides local perception, and a GRU-style gate updates their states. Its hierarchical variant cascades increasingly wide attention windows w {\ensuremath{\in}} {8,32,128} and reuses each stage{'}s parameters for T{\_}s iterations. We train roughly 30M-parameter models on SlimPajama for 60k steps and evaluate on WikiText-103. TextNCA trails a similarly sized Transformer-6L (60.3 versus 52.8 best-run PPL), so we study it as an analytical probe rather than a performance alternative. Reordering or flattening the windows raises PPL by 16.7{--}70.8, whereas a non-iterating sliding-window Transformer preserving the staged order performs comparably across three seeds. These results identify the narrow-to-wide schedule as the dominant factor and provide no reliable evidence that iterated weight sharing improves perplexity at this scale. Intermediate-state analyses support a prepare-then-decode account in which early stages reorganise representations and the final wide stage performs most next-token loss reduction."
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<abstract>Neural Cellular Automata (NCAs) generate complex spatial patterns in vision by repeatedly applying a shared local rule to persistent cell states, combining local perception, weight-shared iteration, and gated in-place updates. We test this principle in language modelling with TextNCA, a 1D causal model in which token positions act as cells, windowed attention provides local perception, and a GRU-style gate updates their states. Its hierarchical variant cascades increasingly wide attention windows w \ensuremathın 8,32,128 and reuses each stage’s parameters for T_s iterations. We train roughly 30M-parameter models on SlimPajama for 60k steps and evaluate on WikiText-103. TextNCA trails a similarly sized Transformer-6L (60.3 versus 52.8 best-run PPL), so we study it as an analytical probe rather than a performance alternative. Reordering or flattening the windows raises PPL by 16.7–70.8, whereas a non-iterating sliding-window Transformer preserving the staged order performs comparably across three seeds. These results identify the narrow-to-wide schedule as the dominant factor and provide no reliable evidence that iterated weight sharing improves perplexity at this scale. Intermediate-state analyses support a prepare-then-decode account in which early stages reorganise representations and the final wide stage performs most next-token loss reduction.</abstract>
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%0 Conference Proceedings
%T TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention
%A Mittal, Avni
%A Anand, Avinash
%A Kumar, Ashutosh
%A Kukreja, Dikshant
%A Prasad, Kritarth
%A Dulloo, Sushane
%A Cambria, Erik
%A Liu, Timothy
%A Wang, Zhengkui
%A Shah, Rajiv Ratn
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F mittal-etal-2026-textnca
%X Neural Cellular Automata (NCAs) generate complex spatial patterns in vision by repeatedly applying a shared local rule to persistent cell states, combining local perception, weight-shared iteration, and gated in-place updates. We test this principle in language modelling with TextNCA, a 1D causal model in which token positions act as cells, windowed attention provides local perception, and a GRU-style gate updates their states. Its hierarchical variant cascades increasingly wide attention windows w \ensuremathın 8,32,128 and reuses each stage’s parameters for T_s iterations. We train roughly 30M-parameter models on SlimPajama for 60k steps and evaluate on WikiText-103. TextNCA trails a similarly sized Transformer-6L (60.3 versus 52.8 best-run PPL), so we study it as an analytical probe rather than a performance alternative. Reordering or flattening the windows raises PPL by 16.7–70.8, whereas a non-iterating sliding-window Transformer preserving the staged order performs comparably across three seeds. These results identify the narrow-to-wide schedule as the dominant factor and provide no reliable evidence that iterated weight sharing improves perplexity at this scale. Intermediate-state analyses support a prepare-then-decode account in which early stages reorganise representations and the final wide stage performs most next-token loss reduction.
%U https://aclanthology.org/2026.findings-emnlp.1413/
%P 30116-30141
Markdown (Informal)
[TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention](https://aclanthology.org/2026.findings-emnlp.1413/) (Mittal et al., Findings 2026)
ACL
- Avni Mittal, Avinash Anand, Ashutosh Kumar, Dikshant Kukreja, Kritarth Prasad, Sushane Dulloo, Erik Cambria, Timothy Liu, Zhengkui Wang, and Rajiv Ratn Shah. 2026. TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 30116–30141, Budapest, Hungary. Association for Computational Linguistics.