NS-ATTENTION: Newton-Schulz Transformations of Attention Outputs in Vision Transformers
Newton-Schulz (NS) iteration has recently been used in the Muon optimizer to transform update matrices during the training of large language models. Motivated by its spectral effect, we investigate applying NS directly to Transformer attention representations. We introduce Newton-Schulz Attention (NS-Attn.), a parameter-free transformation applied to the output of each attention head. Each head output is arranged as a feature-by-token matrix and normalized by its Frobenius norm. We then apply a finite NS polynomial step and restore the original norm. The objective is to reduce spectral concentration and increase effective rank before standard head merging and output projection. Across ViT...
| Authors | Xiaohe Jiang, Guoqiang Zhang, Tianjin Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 September 2026 |
| Last updated at source | 25 September 2026 |
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