hourglass transformer

**Hourglass Transformer** is an **efficient transformer that uses a U-Net-like architecture** — first downsampling the sequence (reducing token count), processing at reduced resolution, then upsampling back, with skip connections preserving fine-grained information. **How Does Hourglass Transformer Work?** - **Downsample**: Reduce sequence length via pooling or strided operations. - **Process**: Apply transformer blocks at the reduced resolution (cheaper attention). - **Upsample**: Restore original sequence length via interpolation or transposed operations. - **Skip Connections**: Concatenate or add features from the downsampling path to the upsampling path. - **Paper**: Nawrot et al. (2022). **Why It Matters** - **U-Net Success**: Brings the highly successful U-Net architecture pattern from vision to sequence modeling. - **Efficiency**: Most computation happens at reduced resolution -> significant speedup for long sequences. - **Quality**: Skip connections preserve fine-grained token-level information despite the compression. **Hourglass Transformer** is **U-Net meets transformers** — compressing, processing, and expanding sequences with skip connections for efficient long-range modeling.