Ontology-Preserving Mapping Theory (OPMT): A Homomorphic Framework for AI Safety and Model Auditing

As Large Language Models (LLMs) and generative AI systems become integral to Research & Development (R&D), the risk of "hallucinations" shifts from semantic incoherence to ontological invalidity — plausible but physically impossible descriptions. This paper formalizes the Artsybashev Analysis Method (AAM-V1) and the AAM-RSL v1.2 (Responsibility & Skepticism Layer) protocol. We introduce the concept of Ontological Homomorphism, a structural mapping Φ: R → M that preserves physical invariants (entropy, energy, causality) between reality (R) and the model (M). Model outputs are classified as VALID (homomorphism preserved), FRINGE (partial homomorphism with large kernel), and GHOST (structural violation). Using the PseudoPhysicsAI case study, we demonstrate how this framework detects subtle violations of thermodynamic laws, providing a rigorous tool for auditing AI in high-responsibility domains.