World Models and Neural Simulators Cheat Sheet

World models and neural simulators represent a paradigm shift in AI from reactive pattern matching to predictive understanding of how environments evolve over time. Unlike large language models that predict discrete tokens, world models learn continuous dynamics of visual, physical, and interactive worlds, enabling agents to imagine futures, plan actions, and learn from simulated experience rather than costly real-world interactions. At their core, world models compress high-dimensional sensory observations (video, depth, semantics) into compact latent representations and predict how those representations transform under actions or time, effectively building an internal physics engine learned from data. This capability is foundational for robotics (sim-to-real transfer, manipulation planning), autonomous systems (driving simulators, trajectory forecasting), reinforcement learning (sample-efficient policy learning via imagination), and generative AI (controllable video synthesis, interactive 3D environments). A key insight: the challenge isn't just generating realistic pixels—it's maintaining temporal coherence, physical plausibility, and causal consistency across long horizons while remaining computationally tractable for real-time planning and control.