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: \emph{token heterogeneity} from multi-modal coupling and spatial variation, and \emph{non-uniform temporal dynamics} where a small set of hard tokens drives error growth, making uniform skipping either unstable or overly conservative. We propose \textbf{WorldCache}, a caching framework tailored to diffusion world models. We introduce \textit{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 \textit{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 \textbf{3.7$\times$} end-to-end speedups while maintaining \textbf{98\%} rollout quality, demonstrating the vast advantages and practicality of WorldCache in resource-constrained scenarios. Our code is released in https://github.com/FofGofx/WorldCache.
LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching
Shanghai Jiao Tong University（上海交通大学）；Shandong University（山东大学）；Terminal Intelligent Computing Division, Alibaba Cloud（阿里云终端智能计算事业部）；South China University of Technology（华南理工大学）；Jilin University（吉林大学）
LazyMAR: Accelerating Masked Autoregressive Models via Feature Caching
Beijing Institute of Technology（北京理工大学）；Central South University（中南大学）；China University of Mining and Technology（中国矿业大学）；University of Electronic Science and Technology of China（电子科技大学）；Tsinghua University（清华大学）；Shanghai Jiaotong University（上海交通大学）
针对 MAR 双向自注意力无法使用传统 KV 缓存的问题，本文提出训练免费、即插即用的 LazyMAR，通过令牌与条件两类特征缓存实现 2.83 倍加速且几乎不损失生成质量。
Accelerating Diffusion Transformers with Token-wise Feature Caching
Shanghai Jiao Tong University（上海交通大学）；University of Electronic Science & Technology of China（电子科技大学）；Sichuan University（四川大学）；National University of Defense Technology（国防科技大学）；Zhejiang University（浙江大学）
针对 Diffusion Transformer 计算成本高的问题，提出逐 token 自适应特征缓存，并按层类型与深度设置缓存比例，在 PixArt-α、OpenSora 和 DiT 上免训练实现近无损加速。
Shanghai Jiao Tong University（上海交通大学）；South China University of Technology（南方科技大学）；Beihang University（北航）；Huawei Technologies Ltd（华为技术有限公司）；Shanghai AI Lab（上海人工智能实验室）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China（人工智能学院，北京师范大学，北京）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China（人工智能与未来网络研究院，北京师范大学，珠海）
MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Data Science & Artificial Intelligence Research Institute, China Unicom（中国unicom数据科学与人工智能研究 institute）；Unicom Data Intelligence, China Unicom（中国unicom数据智能）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers
Shanghai Jiao Tong University（上海交通大学）；Shandong University（山东大学）；University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
DiCache: Let Diffusion Model Determine Its Own Cache
Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；CPII under InnoHK（InnoHK下的CPII）
Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization
Department of Computer Science, North Carolina State University（北卡罗来纳州立大学计算机科学系）；National Center for Computational Science, Oak Ridge National Lab（橡树岭国家实验室计算科学中心）；Department of Mechanical Engineering, Keio University（庆应大学机械工程系）
SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching
Shanghai Jiao Tong University（上海交通大学）；Shandong University（山东大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）
Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor
School of Computer Science, Wuhan University（武汉大学计算机学院）；PaddlePaddle Team, Baidu Inc（百度公司PaddlePaddle团队）；International Joint Innovation Center, The Electromagnetics Academy at Zhejiang University（浙江大学电磁学学院国际联合创新中心）；Yunnan Key Laboratory of Media Convergence（云南媒体融合重点实验室）
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；ByteDance（字节跳动）
Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling
State Key Laboratory of Complex & Critical Software Environment, Beihang University（北京航空航天大学复杂关键软件环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Nanyang Technological University（南洋理工大学）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；ETH Zürich（苏黎世联邦理工学院）
Stochastic Transition-Map Distillation for Fast Probabilistic Inference
Department of Aerospace Engineering, Georgia Institute of Technology（佐治亚理工学院航空航天工程系）；Department of Computer Science, Georgia Institute of Technology（佐治亚理工学院计算机科学系）；Department of Mathematics, Florida State University（佛罗里达州立大学数学系）
Accelerating Streaming Video Large Language Models via Hierarchical Token Compression
EPIC Lab, Shanghai Jiao Tong University（上海交通大学EPIC实验室）；Sichuan University（四川大学）；Huazhong University of Science and Technology（华中科技大学）；Sun Yat-sen University（中山大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Computer Science（计算机科学系）
