WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model

Abstract:Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse intermediate features, selectively update tokens, or reuse and extrapolate denoising outputs ac- cording to local drift or short native-space histories. These criteria can miss both approximation-induced latent transition defects that accumulate across skipped steps and phase- or condition-dependent changes in the direction of latent evo- lution. We propose WorldDynCache, a risk-controlled latent dynamics approximation framework with two core compo- nents. First, a lightweight latent-transition risk estimator tracks the accumulated future impact of approximation defects and calibrates its predictions against counterfactual defects ob- served at exact anchors. Second, a condition- and phase- aware lifted latent surrogate approximates latent evolution without extra transformer evaluations. On HunyuanVoyager- 13B and Aether-5B, WorldDynCache achieves 4.92 times and 2.15 times speedups, respectively, while attaining the best gen- eration quality among the compared caching methods across WorldScore, PSNR, SSIM, and LPIPS.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2608.01845 [cs.LG]

(or arXiv:2608.01845v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.01845 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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