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Robotic agents can now retain crucial visual-motor history within a compact latent transition space, achieving near-perfect recall on complex manipulation tasks with minimal computational overhead.
Transforming object-centric prediction into a semantic interface, SR-WM enables planning-oriented decision-making that captures the complex roles of entities in physical interactions.