The Past Frames the Future: Memory for Autoregressive Video Generation
Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded contex
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Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded contex
This research addresses a critical bottleneck in long-horizon autoregressive video generation, which is essential for developing more capable and persistent AI agents and interactive environments. Overcoming memory limitations is key to scaling video models to real-world complexities and maintaining temporal consistency over extended periods.