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By explicitly enforcing action-conditioned consistency during training and distillation, MWM enables more reliable planning in imagined future spaces for embodied navigation.
Generative video models can now simulate a continuously evolving world, even when objects are out of sight, thanks to a new framework that maintains persistent global state.
RAG agents can be tricked by a "Visual Placebo Effect" where they inherit latent visual biases from foundation models, but a new memory weighting scheme can help them abstain when evidence is weak.
Coding LLMs can now generate more physically plausible and dynamically rich 4D worlds, thanks to a novel closed-loop framework that iteratively refines simulation code based on physics-aware self-reflection.
Achieve zero-shot robotic manipulation by guiding a hierarchical vision-language-action model with knowledge-guided trajectory planning, outperforming existing methods and eliminating the need for real-world data collection.
