Fuzzy-World: Interpretable World Models via Grounded IF–THEN Dynamics

World models support dynamical prediction by learning state transitions. However, aggregate prediction accuracy neither explains the basis of local state changes nor establishes where the learned relationships apply. Here, we introduce Fuzzy-World, an intrinsically interpretable world model combining named physical states with sparse fuzzy rules. Explicit local increment equations and Top-2 activation decompose each predicted physical increment into the weighted contributions of at most two executed rules, directly linking physical semantics, rule explanations, and transition computations. Rules are fitted using inference-time activation weights, with shared dynamics and support-dependent residual shrinkage controlling local corrections. An optional residual flow supplements observation information while keeping physical rules fixed during subsequent learning. Experiments on four dynamical systems show that, among eight dynamics models with observation reconstruction, Fuzzy-World achieves the lowest mean out-of-distribution 50-step observation prediction error and the highest mean physical-trajectory R² on all four systems. Complete rule audits characterize empirical validity within actual activation regions through sample coverage, effective sample size, and local prediction accuracy. These results demonstrate competitive dynamical prediction with sparse, inspectable local rules.