NS-WM: Neural-Symbolic World Modeling under Partial Observability

World models under partial observability must reuse dynamics when familiar sensor views and operating regimes occur in combinations withheld from training. We study this combination out-of-distribution (combination-OOD) setting with NS-WM, a neuro-symbolic framework that separates view-dependent observation mappings from sparse state-action-regime dynamics. This factorization lets the same transition operate with different sensor mappings, while observation-aware state estimation connects masked histories to the state required for rollout. For controlled systems, an encoder route learns symbolic latent dynamics, and a complementary identification route fits dynamics and sensor maps in reference-sensor coordinates and estimates states by matching visible histories. Each route reuses its current state across alternative action plans; validation-selected mixtures combine their observation predictions. Shared temporal and sensor experts extend the prediction interface to heterogeneous sequences. Across six benchmark suites and an additional controlled population, NS-WM reduces combination-OOD mean squared error (MSE) relative to both evaluated baselines in 13/14 condition-population settings. It improves both combination-OOD forecasting and action-effect skill in all nine controlled settings. Controlled diagnostics recover the active dynamics terms, while showing that accurate equations do not eliminate ambiguity in history-based state estimation.