WNet: Discrete Wavelets Transform for Efficient Token Mixing
In a Transformer, token mixing is the step that lets each token draw information from other tokens, and it dominates the cost of encoding long sequences. Self-attention does this mixing very well: every token weighs every other token by content, which gives strong contextual modeling. That all-pairs comparison is also why its cost grows quadratically with sequence length. We introduce WNet, a Transformer encoder that replaces self-attention with token mixing based on the discrete wavelet transform (DWT). Three attention-free mixers recombine the scales: by linear fusion, by learned gating, or by letting each token choose its scales. A hybrid adds self-attention in the last layer only. A r...
| Authors | Rana Aref Salama, Abdou Youssef, Mona Diab |
|---|---|
| Primary category | cs.CL |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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