Learning a unified latent action interface from visual transitions, so world models can transfer action structure across embodiments instead of overfitting to raw robot commands.
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Reconstruction-based latent action models can generate convincing video while barely controlled by the action. Debiasing the latent action space first cuts action-following error by over 30%.
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Contact folds the reachability landscape along a new seam. Interaction-Weighted Resampling restores coverage where it happens — lifting a real air-hockey robot from 5/20 to 12/20.
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What latent space should a world model learn for control? A single linear projection extracts a compact, task-centric state from frozen visual foundations.
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Why predictive structure is not causal structure, and how causal world models can give AI a better target for real-world intelligence.
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A runnable causal analysis agent for discovery, effect estimation, counterfactual reasoning, robustness checks, and inspectable reports.
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Why the next generation of agents needs memory, imagination, modular control, and causal understanding before it acts.
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How causal world models can guide exploration, abstract state and action, verify imagined futures, and improve control.
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