Sinkhorn doubly stochastic attention rank decay analysis
The self-attention mechanism is central to the success of Transformer architectures. However, standard row-stochastic attention has been shown to suffer from significant signal degradation across layers. In particular, it can induce rank collapse, resulting in increasingly uniform token representations, as well as entropy collapse, characterized by highly concentrated attention distributions. Recent work has highlighted the benefits of doubly stochastic attention as a form of entropy regularization, promoting a more balanced attention distribution and leading to improved empirical performance. In this paper, we study rank collapse across network depth and show that doubly stochastic atten...
| Authors | Michela Lapenna, Rita Fioresi, Bahman Gharesifard |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 6 October 2026 |
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