MoSAR: Mixture of Semantic Attention Regimes for Learning Adaptive and Approximable Attention Geometries

The quadratic complexity of dense self-attention remains a central bottleneck for long-context language modeling. Many efficient alternatives address this cost by deciding in advance where attention should be sparse or local. We argue that attention approximation should instead be approached as a ge