AI RESEARCH
Latent Geometry Beyond Search: Amortizing Planning in World Models
arXiv CS.LG
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ArXi:2605.08732v1 Announce Type: cross Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging. This raises a central question: when does a learned representation simplify control, rather than merely enabling prediction? We study this question in a pretrained LeWorldModel, whose latent geometry is regularized for smoothness and uniformity.