I unified convolution and attention into a single framework
The operational primitives of deep learning, primarily matrix multiplication and convolution, exist as a fragmented landscape of highly specialized tools. This paper introduces the Generalized Windowed Operation (GWO), a theoretical framework that unifies these operations by decomposing them into three orthogonal components: Path, defining operational locality; Shape, defining geometric structure and underlying symmetry assumptions; and Weight, defining feature importance. We elevate this f