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Long-WAM is presented, a model-system framework for scaling the context of causal world-action models under real-time control constraints, and finds that access to history is not the same as using it: longer histories pay off far more when the video foundation is pretrained autoregressively (AR).
Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillation stage, for example to accelerate sampling or to improve long-video generation. This stage is typically repeated for every specialized model. We introduce LongLive-Pl...
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,...
ReWorld separates the two during training and bounds them at inference, and under a three-axis protocol covering action following, long-horizon recall, and video quality, against six recent interactive world models it attains the best control fidelity.
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