# A Tutorial on World Models and Physical AI

arXiv: https://arxiv.org/abs/2606.12783 · HTML: https://mlsanity.com/paper/2606.12783/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 0 of its 49 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Built on by (most cited first, 1 of 1)

- [Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation](https://mlsanity.com/paper/2607.28226/) (2026)

## Abstract (arXiv)

World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making. A central distinction can be drawn between explicit world models, which learn structured dynamics for rollout-based reasoning and planning, and implicit world models, which encode predictive structure within scalable learned representations. These complementary paradigms provide a foundation for physical AI in domains such as robotics and autonomous driving, enabling intelligence beyond reactive control under real-world constraints. Recent foundation models further suggest a pathway toward unified systems integrating perception, prediction, and action. Despite rapid progress, major challenges remain in hierarchical reasoning, long-horizon planning, and autonomous goal formation, which are critical for advancing toward artificial general intelligence. This tutorial presents a coherent framework in which diverse world modeling approaches are unified through shared predictive structure and differentiated by how such structure is represented and exploited.