Seonglae ChoCurrently, the best world modeling approaches are Noise Reduction for visual processing and Attention Mechanism for language processing.
Any AI agent capable of multi-step goal-directed tasks must possess an accurate internal World Model through constructive proof that such models can be extracted from agent policies with error bounds. The more an agent learns (higher experience), the better it becomes at solving "deeper" goals, and we can reconstruct transition probabilities more accurately just by observing its policy. There is no "model-free" shortcut: The ability to achieve long-term and complex goals inherently requires learning an accurate world model. However, it is not necessary to explicitly define and train the world model, rather, defining good Next Token Prediction and appropriate implicit AI Incentive is sufficient.
World model Interpretability with Internal Interface Theory
If the way AI interacts with various modules through internal interfaces is consistently formed, the possibility increases that humans can understand the format of these interfaces and interpret the entire world model at once.
