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

2026-09-14

AI Summary

研究通过对比循环和堆叠的Transformer编码器在12导联心电图分类任务上的表现，发现bViT模型尽管参数减少了8.9倍，但准确率与标准ViT相当，且两者动态特性不同。

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.

Problem

• Transformer encoders
• weight-tied
• stacked layers
• representation dynamics
• ECG classification

Innovation

• Recurrent Transformers
• weight-tied block
• latent representations
• geometric similarity metrics
• out-of-distribution feature expansion