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MLLMs can be blind to the consequences of their actions, and simply scaling model size won't fix the problem.
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.
By grounding reasoning within the topology of a global interaction graph, ManCAR achieves up to 46.88% relative improvement in NDCG@10 compared to state-of-the-art sequential recommendation baselines.
