Why Attend to Everything? Focus is the Key
Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into pretrained models, they often degrade perplexity, downstream accuracy, or both. We introduce Focus, a method that learns which token pairs matter. Focus adds a small set of learnable centroids--as few as 148K parameters per layer--that act as gates: only token pairs belonging to the same centroid group attend to each other over long ranges. Focus is composable: it can be added to any pretrained model by training only the centroids while keeping all original weights frozen. Experiments show that composing Focus onto pretrained models yields zero degra...
| Authors | Hengshuai Yao, Xing Chen, Ahmed Murtadha et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 29 April 2026 |
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