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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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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