RESEARCH PAPERYear 2026
Learning Latent Action World Models In The Wild
Classification
View four quadrants- Major category
- WAMs
- Quadrant
- Not applicable
- Architecture
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- Prediction paradigm
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- Source review status
- Verified from primary sources
Category review. This work learns inverse-transition latent actions and latent forward dynamics, then trains a controller mapping candidate real actions into that latent space. CEM searches predicted trajectories toward a visual goal. This is an actionable world-model planning system; V-JEPA is the reusable encoder component. Reading evidence
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Abstract
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