eration</h2>
            <p class="paper-summary">The computational demands of self-attention mechanisms pose a critical
challenge for transformer-based video generation, particularly in synthesizing
ultra-long sequences. Current approaches, such as factorized attention and
fixed sparse patterns, fail to fully exploit the inherent spatio-temporal
redundancies in video data. Through systematic analysis of video diffusion
transformers (DiT), we uncover a key insight: Attention matrices exhibit
structured, yet