Not All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels

Linearizing softmax attention offers a route to efficient Transformer inference, but preserving pretrained attention remains challenging. We identify an important source of approximation error overlooked by standard linearization methods: a small number of outlier dimensions contribute disproportion