Adaptive Small Worlds: Query-Sufficient Compilation for Efficient World-Model Planning

World models enable planning by simulating actions, but a model broad enough to support many tasks may be unnecessarily large for any single decision. Moreover, a planning query may depend on only a few state factors such as object locations and resource counts. Thus, planning in the full model spends rollouts on irrelevant factors, whereas aggressive pruning can cause failures. We propose Adaptive Small Worlds (ASW), a test-time compiler that can reduce an existing world model to a query-specific small world, i.e., a planning model of selected factors, action effects, and rewards. It selects factors from a supplied state description via agent-generated rules to build a small world, then uses dependency links built from training traces to add prerequisites and necessary missing state factors. ASW searches for this reduced model, decodes proposals into environment actions, and checks their initial steps in the original model within the same transition budget. Our analysis shows that query-specific representations can require exponentially fewer states than a shared representation, and bounds the expected cumulative reward loss relative to planning in the original model under stated assumptions. Across BabyAI, QueryStress, and symbolic Craftax, our proposed ASW improves the success–budget frontier over both static abstraction, query-based baselines, and conventional planners. These results support our claim: efficient planning should first compile the smallest executable world that preserves the present decision while retaining a well-formed route back to the full one.