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.

World models can help AI systems imagine how scenes may change over time, but high-quality models are often too slow for interactive or resource-limited use. This paper introduces WorldCache, which speeds up such models without extra training by reusing reliable internal information and recomputing only the parts that are likely to change unpredictably. By focusing computation on the difficult parts of generation, WorldCache keeps the results stable while avoiding much unnecessary work. Experiments show that it can make diffusion-based world models up to 3.7× faster while preserving most of the original quality.