WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching

Diffusion-based world models have shown strong potential for unified world simulation, but the iterative denoising remains too costly for interactive use and long-horizon rollouts. While feature caching can accelerate inference without training, we find that policies designed for single-modal diffusion transfer poorly to world models due to two world-model-specific obstacles: *token heterogeneity* from multi-modal coupling and spatial variation, and *non-uniform temporal dynamics* where a small set of hard tokens drives error growth, making uniform skipping either unstable or overly conservative. We propose **WorldCache**, a caching framework tailored to diffusion world models. We introduce *Curvature-guided Heterogeneous Token Prediction*, which uses a physics-grounded curvature score to estimate token predictability and applies a Hermite-guided damped predictor for chaotic tokens with abrupt direction changes. We also design *Chaotic-prioritized Adaptive Skipping*, which accumulates a curvature-normalized, dimensionless drift signal and recomputes only when bottleneck tokens begin to drift. Experiments on diffusion world models show that WorldCache delivers up to **3.7$\times$** end-to-end speedups while maintaining **98\%** rollout quality, demonstrating the vast advantages and practicality of WorldCache in resource-constrained scenarios.