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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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Key Takeaways

  • The V–M–C decomposition is a practical way to reason about world models
  • Training stability improves with curriculum, short-to-long horizon growth, and appropriate regularization
  • Uncertainty handling is central to safe and effective planning
  • Tailor designs to domain constraints and safety requirements
  • Evaluate beyond prediction error: include planning outcomes, calibration, and compute/latency budgets
Section 10 of 12
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