WorldState: Scalable Implicit Memory for Interactive Video World Models

Interactive video world models generate videos conditioned on user inputs and camera movements. Long-horizon interaction requires these models to preserve previously generated environments and recover historically consistent content during spatial revisits. Explicit memories either require growing storage and retrieval or rely on potentially inaccurate geometric estimates. Recurrent linear memory avoids these costs but suffers from imbalanced frame-wise updates and fixed capacity: spatial normalization weakens erasure relative to writing, while a single state cannot scale with video length, forcing a growing amount of historical information to compete for limited memory capacity. To address these limitations, we propose WorldState, a scalable implicit memory model built upon a hybrid attention backbone. WorldState decouples memory erasing and writing for fine-grained memory editing and uniformly organizes history into a logarithmically growing set of temporally isolated states. Completed historical states remain subject to channel-wise decay but are excluded from subsequent erasure and writing, reducing repeated overwriting while preserving adaptive forgetting. Context-aware memory routing further retrieves relevant historical states according to the current generation context. Extensive experiments demonstrate that WorldState substantially improves long-term memory retention and spatial revisit consistency while maintaining scalable long-horizon generation efficiency, enabling high-fidelity and spatially coherent video generation over extended interaction horizons.