What should a world model actually be designed to do?
Current AI research treats world models as either video predictors or RL dynamics learners, but what if their real purpose is simulating actionable possibilities for decision-making rather than predicting next observations?
World models decompose into five inseparable design choices. LLMs extract world facts but lack mechanistic world models Social simulation needs cognitive models not just behavioral prediction Training regime matters more than inference compute budget Linguistic agency requires embodiment, participation, and precariousness LLMs lack
Why do foundation models develop heuristics instead of world models? How do training objectives shape what a world model actually learns? Can a world model have rich representations without adequate data coverage? Why does integrating world models with decision-making systems matter? Why must world models be nested rather than flat and uniform? Do LLMs need world models to make accurate predictions? What distinguishes task-specific heuristics from genuine world models? What's the difference between representing world facts and generating world mechanisms?