AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LM-X is introduced, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence and shows that explicit multi-timescale predictive state can strengthen control while exposing interpretable internal estimates.
This work introduces stylized phase manifolds—a compact, interpretable latent representation that disentangles motion content, the temporal structure, and style and develops a diffusion‐based motion generator that enables fine‐grained control over semantic, temporal, and stylistic aspects of motion.
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