# SpectralCache

The work reveals that diffusion-based world-model features have highly stable singular subspaces across nearby denoising steps with predictable singular-value evolution, and builds on this to propose SpectralCache, a training-free spectral caching framework that reuses stable singular subspaces and estimates only low-dimensional singular values via linear extrapolation, while exploiting spectral consistency between neighboring full-computation features to skip selected expensive backbone evaluations via singular value scaling; on HunyuanWorld-Voyager-13B it achieves 5.22x acceleration while maintaining a WorldScore of 65.90 for static scenes, substantially outperforming existing training-free caching methods in inference efficiency.

The work reveals that diffusion-based world-model features have highly stable singular subspaces across nearby denoising steps with predictable singular-value evolution, and builds on this to propose SpectralCache, a training-free spectral caching framework that reuses stable singular subspaces and estimates only low-dimensional singular values via linear extrapolation, while exploiting spectral consistency between neighboring full-computation features to skip selected expensive backbone evaluations via singular value scaling; on HunyuanWorld-Voyager-13B it achieves 5.22x acceleration while maintaining a WorldScore of 65.90 for static scenes, substantially outperforming existing training-free caching methods in inference efficiency.