Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers
The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales quadratically with the sequence length, serving as a major bottleneck in long-context language modeling. Sliding window attention restricts the context length for better efficiency at the cost of narrower receptive fields. While existing efforts attempt to take the benefits from both sides by building hybrid models, they often resort to static, heuristically designed alternating patterns that limit efficient allocation of computation in various scenarios. In this paper, we propose Switch Attention (SwiAttn), a novel hybrid transformer that ena...
| Authors | Yusheng Zhao, Hourun Li, Bohan Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 March 2026 |
| Last updated at source | 29 August 2026 |
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