## Web World Models – Simple Explainer
What can be possible with advances in WWMs
The ability to train agents in realistic environments before being shipped to production, will unlock a new category in agent training infrastructure.
Problem: You can’t store an infinite world in a database. But agents need persistent environments. Places they can leave, come back to, and find exactly as they left them.
Right now there are two ways to build agent “simulators”:
Traditional software – Reliable but rigid. The agent can only do what developers explicitly coded. Like a flight sim where you can only fly to 5 airports.
Pure AI generation – The AI hallucinates the entire world, but nothing stays consistent. You fly back to an airport and suddenly it has 8 runways instead of 3.
Solution: Princeton’s new paper found a fix. Use regular code to enforce the rules (physics, inventory limits, what’s allowed) and let AI handle the texture (descriptions, dialogue, atmosphere). Every location has a unique address. That address always generates the same place – same buildings, same layout, same details. They are replacing storage with just math. They call it Web World Models (WWM).
Think video games where the engine handles character physics and health bars, the graphics layer handles the environment generation on the fly. So in GTA you can’t drive through buildings or spend money you don’t have but the streets could still feel alive with pedestrians and chatter.
Robots need the same thing – persistent, consistent environments to practice in before touching the real world. Warehouses, kitchens, factory floors where robots need to fail safely before touching the real world. The ability to train agents and robots in realistic environments before being shipped to production will unlock a new category in agent training infrastructure. WWMs offer a path to build those at scale.
How are you training and evaluating your agents today? What does your RL environment setup look like? If you’re building company to solve this problem email us deals@array.vc.
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