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This paper proposes a four-level framework classifying agentic visual generation systems based on the controller's direct decision scope over generation operations, from fixed support to experience-adaptive control.
This paper presents a goal-agnostic control framework for partial differential equations using a joint-embedding predictive architecture (JEPA) with a lightweight 2D ViT encoder and action-conditioned latent dynamics, showing that using a learned physical observable probe outperforms raw latent distance for control tasks.