By skyforbes 
PAN is a world model that learns from video datasets and allows AI agents to predict and plan future actions over extended periods. The model uses natural language for interaction, connecting human instructions with complex physical simulations.
We see language models generating coherent, context-aware text, video generators producing visually convincing sequences, and reinforcement learning agents mastering complex games. The critical limitation in current AI systems is their inability to interactively anticipate and consistently simulate how events unfold in the real world. While AI has made significant strides, most models struggle with long-term, causally coherent predictions, they fail to accurately forecast future states based on current actions and interactions in a reliable, continuous way. They can predict what might happen in a narrow, immediate context but fail when asked to reason across extended sequences of events or respond to specific actions over time.
Predictive Action Network is a groundbreaking approach that aims to bridge this gap. It is a general, interactable, long-horizon world model that predicts how the world evolves conditioned on both its history and natural-language-specified…