AI 101: What are Web World Models?

World models are becoming essential because language-based agents increasingly need environments that persist over time. Agents have to remember past actions, observe consequences, and keep interacting rather than reset after each step. To support this, researchers are building simulated worlds that can train other models, imitate real-world situations, and allow behavior to unfold over long horizons.

While engineers and developers are still exploring the best technical approaches, the requirements are already clear. A useful simulated AI world must be controllable, consistent, and open-ended.

Until recently, we had two main approaches to building worlds for AI agents, and neither met all the requirements at once:

• Traditional web applications store state in databases and operate under fixed rules. They are stable and controllable, but limited to what developers specify in advance.

• Fully generative worlds place AI models at the center, generating places, events, and even 3D scenes on the fly. These systems are flexible, but difficult to control or constrain.

Researchers from Princeton University and the University of California propose a simple but effective alternative: building rich, open-ended AI worlds using standard web technology. Hard rules, the “physics” of the world, remain in code, while AI models are layered on top to generate narratives, descriptions, and high-level decisions. They call this approach the Web World Model (WWM), offering a middle ground between structure and flexibility.

Let’s walk through how this works from start to finish.

In today’s episode, we will cover:

• Web World Models (WWMs): What’s new in design?

• The variety of WWM applications

• How are WWM and Neuro-Symbolic AI connected?

• Advantages of WWMs as a new world model concept

• Not without limitations

• Conclusion

• Sources and further reading

Web World Models (WWMs): What’s new in design?

By now, the rules of the game are more or less defined – building a reliable world for AI agents means balancing strict rules with creative generation. Princeton University and the University of California’s new concept – a Web World Model (WWM) – builds around exactly these two pieces: solid, deterministic code that gives structure and probabilistic language model (LM) that add richness and variety. Ordinary web code includes things, such as TypeScript modules, HTTP handlers, database schemas, etc.

The researchers outlined four foundational principles that make WWMs work in practice – and this is a real roadmap you can stick to:

1. Separate rules from imagination

A WWM splits the world into two layers, similar to how video games separate game logic from graphics.

• The physical layer is handled by code. It is fully deterministic and implemented in normal web code. This is the part that guarantees consistency. It stores and updates:

  • Positions and coordinates

  • Inventories and resources

  • Obviously, rules like “you can’t open a locked door”, “you can’t spend money you don’t have”

• Imagination layer is the responsibility of an LM. It produces:

  • Descriptions of places

  • NPC dialogue

  • Atmosphere, tone, and narrative details

General updates happen in steps: 1) The user takes an action; 2) Code first computes the new logical state; 3) Only then does an LLM generate descriptions based on that updated state. Since logic always comes first, the world stays coherent, even when the model expresses the full range of creativeness.

2. Typed web interfaces for LLMs

In WWMs, models don’t operate on hidden embeddings, and free-form text or vectors don’t properly connect the code and the model parts. WWMs store the world’s hidden state in structured, typed web interfaces. So a model outputs JSON that must match a schema, for example, like this: interface Planet {biome: string; hazard: string;}. This makes outputs inspectable and debuggable, and every generated object contains exactly the information the physics layer needs. TypeScript modules enforce these typed interfaces between code and models.

3. Deterministic hashing

You can’t store an infinite world in a database, but you can recreate it consistently.