@inproceedings{rodkin-etal-2026-beyond,
title = "Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling",
author = "Rodkin, Ivan and
Orel, Daniil and
Smirnov, Konstantin and
Bolatov, Arman and
Elbouardi, Bilal and
Hassan, Besher and
Kuratov, Yuri and
Bulatov, Aydar and
Nakov, Preslav and
Baldwin, Timothy and
Shelmanov, Artem and
Burtsev, Mikhail",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2103/",
pages = "42385--42404",
ISBN = "979-8-89176-395-1",
abstract = "Reasoning is a core capability of large language models (LLMs), yet how multi-step reasoning is learned and executed remains unclear. We study this question in a controlled cellular-automata (1dCA) framework that excludes memorization by using disjoint training and test rules. Given a short state sequence, the model is required to infer the hidden local rule and then chain it to predict multiple future steps. Our evaluation shows that LLMs largely fail to reliably solve a natural-language proxy of the proposed task. We find that most neural architectures trained from scratch can learn rule inference and achieve high next-step accuracy, but performance drops sharply as the required number of intermediate reasoning steps increases. Experiments show that increasing model depth is crucial, and extending effective depth via recurrence, memory, or test-time compute improves results but remains bounded. Code is available on github: https://github.com/RodkinIvan/associative-recurrent-memory-transformer/tree/ACT."
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<abstract>Reasoning is a core capability of large language models (LLMs), yet how multi-step reasoning is learned and executed remains unclear. We study this question in a controlled cellular-automata (1dCA) framework that excludes memorization by using disjoint training and test rules. Given a short state sequence, the model is required to infer the hidden local rule and then chain it to predict multiple future steps. Our evaluation shows that LLMs largely fail to reliably solve a natural-language proxy of the proposed task. We find that most neural architectures trained from scratch can learn rule inference and achieve high next-step accuracy, but performance drops sharply as the required number of intermediate reasoning steps increases. Experiments show that increasing model depth is crucial, and extending effective depth via recurrence, memory, or test-time compute improves results but remains bounded. Code is available on github: https://github.com/RodkinIvan/associative-recurrent-memory-transformer/tree/ACT.</abstract>
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%0 Conference Proceedings
%T Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
%A Rodkin, Ivan
%A Orel, Daniil
%A Smirnov, Konstantin
%A Bolatov, Arman
%A Elbouardi, Bilal
%A Hassan, Besher
%A Kuratov, Yuri
%A Bulatov, Aydar
%A Nakov, Preslav
%A Baldwin, Timothy
%A Shelmanov, Artem
%A Burtsev, Mikhail
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F rodkin-etal-2026-beyond
%X Reasoning is a core capability of large language models (LLMs), yet how multi-step reasoning is learned and executed remains unclear. We study this question in a controlled cellular-automata (1dCA) framework that excludes memorization by using disjoint training and test rules. Given a short state sequence, the model is required to infer the hidden local rule and then chain it to predict multiple future steps. Our evaluation shows that LLMs largely fail to reliably solve a natural-language proxy of the proposed task. We find that most neural architectures trained from scratch can learn rule inference and achieve high next-step accuracy, but performance drops sharply as the required number of intermediate reasoning steps increases. Experiments show that increasing model depth is crucial, and extending effective depth via recurrence, memory, or test-time compute improves results but remains bounded. Code is available on github: https://github.com/RodkinIvan/associative-recurrent-memory-transformer/tree/ACT.
%U https://aclanthology.org/2026.findings-acl.2103/
%P 42385-42404
Markdown (Informal)
[Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling](https://aclanthology.org/2026.findings-acl.2103/) (Rodkin et al., Findings 2026)
ACL
- Ivan Rodkin, Daniil Orel, Konstantin Smirnov, Arman Bolatov, Bilal Elbouardi, Besher Hassan, Yuri Kuratov, Aydar Bulatov, Preslav Nakov, Timothy Baldwin, Artem Shelmanov, and Mikhail Burtsev. 2026. Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 42385–42404, San Diego, California, United States. Association for Computational Linguistics.