critical tokens}; a fine stage computes
token-level attention only inside those tiles subjecting to block computing
layout to ensure hard efficiency. This leads to a single differentiable kernel
that trains end-to-end, requires no post-hoc profiling, and sustains 85\% of
FlashAttention3 MFU. We perform a large sweep of ablation studies and
scaling-law experiments by pretraining DiTs from 60M to 1.4B parameters. VSA
reaches a Pareto point that cuts training FLOPS by 2.53$\times$ with no drop in
diffusion los