arxiv.orgpaper

The Past Frames the Future: Memory for Autoregressive Video Generation

A systematic survey of memory mechanisms in autoregressive (AR) video generation, addressing how models maintain persistent historical information (entity identities, dynamic states, causal changes) beyond bounded context windows. The authors organize the literature into five perspectives — Forms, Functions, Operations, Learning, and Evaluation — and identify open challenges like composable memory architectures and standardized evaluation for memory-conditioned video generation.