Context Persistence Calibration for Interactive Long Video Generation

Interactive long video generation entails adapting to successive prompts while preserving coherent content over extended horizons. Causal autoregressive models are well suited to this setting, as sequential generation and KV cache reuse naturally support streaming synthesis. Existing methods primarily preserve historical context through visual KV caches, while paying comparatively limited attention to the persistence of textual conditioning. Yet textual conditioning provides direct semantic guidance by specifying the desired visual content. In interactive settings, the initial prompt often establishes global attributes such as subject appearance, scene, and visual style. As generation proceeds under successive prompts, relying primarily on cached visual states to preserve these global attributes becomes increasingly unreliable, degrading long-range visual consistency and semantic coherence. To address this limitation, we propose Context Persistence Calibration (CPC), a training-free framework that maintains global semantics while accommodating evolving instructions. Specifically, we jointly contextualize the initial and current prompts and retain the leading singular components of the contextualized initial-prompt representation. We term this procedure Semantic Anchor, which constructs a rank-constrained textual reference at each subsequent interaction stage. To further mitigate sink-collapse and the resulting abrupt scene resets, we introduce Adaptive Sink Regulation, which adaptively regulates excessive contributions from the frame sink. CPC operates entirely at inference time without additional training, learnable parameters, or cache expansion. Experiments on two interactive long video evaluation sets, InterVidProM and InterStoryBench, demonstrate improved long-range visual consistency and semantic coherence, reduced sink-collapse, and continued responsiveness to evolving prompts.