Same path, different: a mechanistic comparison of looped and stacked transformer encoders on 12-lead ECG

Abstract:Recurrent Transformers reusing their weights rather than stacking $L$ distinct layers are becoming widely adopted due to their parameter efficiency [1,2,3]. However, the exact representational and dynamical differences between looped and stacked architectures remain uncharacterized. This paper presents a controlled study on the example of bViT model [1] applying one weight-tied block $L$ times. We train two models: bViT and standard ViT [4] on 12-lead electrocardiogram (ECG) classification tasks from the PTB-XL dataset under identical training protocols. Despite an $8.9\times$ parameter reduction, bViT achieves accuracy parity with ViT. Geometric similarity metrics demonstrate that both architectures construct comparable latent representations in an equivalent canonical order. Crucially, their dynamics differ: bViT exhibits smaller step sizes and inter-patient sensitivity, as well as near-neutral behavior away from the data manifold, whereas ViT exhibits collapsing dimensionality of representations and out-of-distribution feature expansion.

Machine Learning (cs.LG); Dynamical Systems (math.DS)

arXiv:2609.15498 [cs.LG]

(or arXiv:2609.15498v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.15498 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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