AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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...
Unified Multimodal Models (UMMs) often rely on separate visual representations for understanding and generation, increasing visual context length and complicating integration with established vision-language pretraining pipelines. Recent advances in pixel-space modeling offer an encoder-free alternative, but extending...
Context-Matched Distillation (CMD) is introduced, a causal DMD framework that aligns teacher supervision with the information available when each target is generated, and naturally extends to frame-wise and chunk-wise generation, long video distillation, and camera-conditioned distillation.
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