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World Models in AI Systems

Advanced AI architectures that learn environment dynamics for simulation, prediction, and planning in robotics, gaming, and autonomous systems

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Common Pitfalls and Remedies

  • Model collapse/overconfidence: Add regularization; enforce entropy on mixture outputs; calibrate uncertainty with ensembles.
  • Compounding errors: Limit rollout horizons, replan frequently, use consistency losses, and incorporate closed-loop training signals.
  • Exploiting unrealistic simulations: Tighten fidelity constraints; penalize unrealistic states; incorporate real-data anchors.
  • Domain shift: Train with augmentations and diverse seeds; add OOD detectors and conservative fallback behaviors.
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