Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling

ACL ARR 2026 January Submission10377 Authors

Keywords: sequence models, reasoning, transformers, state-space models, recurrent models, recurrent transformers, adaptive computation time, computation expressivity, computational complexity, chain-of-thoughts, reinforcement learning

Abstract: Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. We study this question in a controlled cellular-automata (1dCA) framework that excludes memorisation 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.

Paper Type: Long

Research Area: Mathematical, Symbolic, Neurosymbolic, and Logical Reasoning

Research Area Keywords: symbolic reasoning, logical reasoning, inductive reasoning

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 10377