Domain-Aware Hallucination Prevention: Configurable Confidence Policies for Safety-Critical Neurosymbolic AI

This paper presents a domain-specific confidence policy framework that extends neurosymbolical routing architectures with configurable per-domain and per-capability confidence thresholds. While neurosymbolic AI systems achieve zero hallucination through provenance-based reasoning, uniform confidence thresholds fail to capture domain-specific safety requirements. The framework introduces a Domain Confidence Registry, Capability Profiles, Graduated Response Policies, and a four-tier Safety Classification Hierarchy. Validation demonstrates zero safety violations without sacrificing coverage, sub-millisecond routing overhead (0.0013ms P95), and 100% classification accuracy across six domains.