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Text embeddings can predict item difficulty with surprising accuracy, but the predictability of other parameters is limited by their inherent reliability ceilings.
Ditch VAEs and AMP: SLMP learns structured motion priors in a spherical latent space, enabling stable random sampling of diverse and valid humanoid behaviors without information loss.
