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The CoTFormer architecture proposes formalizing Chain-of-Thought reasoning as recurrent latent computation, aiming to improve out-of-distribution generalization on inductive tasks while maintaining intermediate states for attention mechanisms. This research directly addresses core challenges in machine learning model efficiency and interpretability by integrating explicit reasoning traces into transformer structures. The study evaluates these structural variants against standard perplexity metrics within controlled algorithmic settings to validate their theoretical benefits.
Reproducing Recurrent Transformers: The CoTFormer
arXiv:2607.19405v1 Announce Type: new Abstract: The CoTFormer architecture formalizes Chain-of-Thought as a form of recurrent latent computation, preserving intermediate states as attendable representations to mimic explicit reasoning traces. In this work, we evaluate CoTFormer and its structural variants across perplexity and compute efficiency metrics. Furthermore, we extend evaluation to controlled algorithmic settings to determine whether this recurrent framework improves out-of-distribution generalisation on inductive reasoning tasks.
Abstract:The CoTFormer architecture formalizes Chain-of-Thought as a form of recurrent latent computation, preserving intermediate states as attendable representations to mimic explicit reasoning traces. In this work, we evaluate CoTFormer and its structural variants across perplexity and compute efficiency metrics. Furthermore, we extend evaluation to controlled algorithmic settings to determine whether this recurrent framework improves out-of-distribution generalisation on inductive reasoning tasks.
Machine Learning (cs.LG)
arXiv:2607.19405 [cs.LG]
(or arXiv:2607.19405v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.19405 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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