[numpy-thagoras]

A world model is a persistent representation of the world (however compressed) that is available to an AI for accessing and compute. For example, a weather world model would likely include things like wind speed, surface temperature, various atmospheric layers, total precipitable water, etc. Now suppose we provide a real time live feed to an AI like an LLM, allowing the LLM to have constant, up to date weather knowledge that it loads into context for every new query. This LLM should have a leg up in predictive power.

Some world models can also be updated by their respective AI agents, e.g. "I, Mr. Bot, have moved the ice cream into the freezer from the car" (thereby updating the state of freezer and car, by transferring ice cream from one to the other, and making that the context for future interactions).

[bigyabai]

If your "world model" only models a small portion of the world, I think the more appropriate label is a time-series model. Once you truncate correlated data, the model you're left with isn't very worldly at all.

[numpy-thagoras]

You don't need to load the entire world model in order to be effective at a task. LLM providers already do something similarly described with model routing.