Anisotropic Representations Improve Planning in JEPA World Models
From the abstract
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning.
From the abstract. Our summary is in progress.