aster Video Diffusion with Trainable Sparse Attention</h2>
            <p class="paper-summary">Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D
attention, even though most of the attention mass concentrates on a small
subset of positions. We turn this observation into VSA, a trainable,
hardware-efficient sparse attention that replaces full attention at \emph{both}
training and inference. In VSA, a lightweight coarse stage pools tokens into
tiles and identifies high-weight \emph{