This paper presents a comprehensive analysis of cross-domain transfer learning in Latent Posterior Factors (LPF), comparing two architectural variants: LPF-SPN (Sum-Product Network aggregation) and LPF-Learned (neural aggregation). We evaluate zero-shot transfer from a compliance domain to six target domains—healthcare, academic, construction, legal, finance, and materials—and demonstrate significant improvements through few-shot adaptation. Our results reveal fundamental trade-offs between structural probabilistic reasoning and learned neural aggregation in cross-domain scenarios, with LPF-Learned achieving 42.5% average zero-shot accuracy compared to LPF-SPN's 29.0%. We further show that domain adaptation with 100-shot learning improves performance to 46.3% average accuracy while maintaining superior calibration (ECE: 0.147 vs. 0.253 zero-shot). These findings provide actionable guidance for practitioners deploying probabilistic reasoning systems across heterogeneous domains. Keywords:Cross-domain transfer learning, Latent Posterior Factors (LPF), neuro-symbolic AI, probabilistic reasoning, neural aggregation, sum-product networks (SPN), zero-shot learning, few-shot adaptation, uncertainty calibration, domain adaptation, multi-domain AI, evidence aggregation, machine learning robustness, structural vs. learned aggregation, interpretable AI, heterogeneous data, ECE calibration, applied AI systems