OPEN WORLDS NEED RULE DISTILLATION

Open-world AI systems are rapidly expanding through larger content spaces, richer task sets, more coherent agent behaviors, and more flexible language interfaces. This position paper argues that such progress masks a more fundamental bottleneck: systems may appear open because agents can see, say, or attempt more, while remaining closed at the mechanism layer that determines what the world can recognize, validate, execute, and reuse. We call this mismatch the content trap. We argue that the next bottleneck of open-world AI is not generating more content, tasks, or behaviors, but enabling the endogenous growth of executable world mechanisms. To this end, we propose rule distillation: treating LLM-generated intents, justifications, and consequence predictions not as direct world updates, but as candidate rule priors that must earn validity through mechanistic formalization, execution, replay validation, audit tracing, and failure rollback before becoming reusable world mechanisms. Through a scarcity-driven trajectory in the Farm-Town simulator, we illustrate how one-off generated behavior can be distilled into executable, verifiable, and reusable mechanisms. We therefore argue that the core progress of open worlds is not to generate more content, but to gradually acquire the ability to simulate real-world rules.