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LRC-JEPA: Disentangling Dynamics and Residual Context for Efficient World Models
Compact JEPA world models enable efficient latent-space planning, but low-dimensional representation trained under reward-free self-supervision must encode both action-conditioned dynamics and predictable visual context. This competition can entangle controllable state with high-rank nuisance appear
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