PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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Proposes a three-part generative anonymization pipeline using disentangled variational encoding, manifold-aware identity replacement, and distilled latent diffusion to protect face identities in MRAG while preserving non-identity attributes.
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Physically Interpretable World Models via Weakly Supervised Representation Learning
PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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Identity-Decoupled Anonymization for Visual Evidence in Multi-modal Retrieval-Augmented Generation
Proposes a three-part generative anonymization pipeline using disentangled variational encoding, manifold-aware identity replacement, and distilled latent diffusion to protect face identities in MRAG while preserving non-identity attributes.