Pretraining Frame Preservation for Lightweight Autoregressive Video History Embedding

ArXi:2512.23851v4 Announce Type: replace Autoregressive video generation relies on history context for content consistency and storytelling. As video histories grow longer, efficiently encoding them remains an open problem - particularly for personal users and local workflows where compute and memory budgets are limited. We present a lightweight history encoder that maps long video histories into short-length embeddings, pretrained with a frame query objective that learns to attend to content features at arbitrary temporal positions. The pre.