Ontology-Preserving Mapping Theory (OPMT): A Homomorphic Framework for AI Safety and ModelauditingAndrey A. ArtsybashevIndependent Researcher, Kharkiv, UkraineIdentifier: AAM-V1ARTSYBASHEVUAKHARKIVAIANALYSISFebruary 9, 2026AbstractAs Large Language Models (LLMs) and generative AI systems become integral to Research& Development (R&D), the risk of “hallucinations” shifts from semantic incoherence to on-tological invalidityplausible but physically impossible descriptions. This paper formalizes theArtsybashev Analysis Method (AAM-V1) and the AAM-RSL v1. 2 (Responsibility& Skepticism Layer) protocol. We introduce the concept of Ontological Homomorphism, astructural mapping Φ: R → M that preserves physical invariants (entropy, energy, causality) between reality (R) and the model (M). We classify model outputs into VALID (homomor-phism preserved), FRINGE (partial homomorphism with a large kernel), and GHOST (structural violation). Using the PseudoPhysicsAI case study, we demonstrate how thisframework detects subtle violations of thermodynamic laws, providing a rigorous tool forauditing AI in high-responsibility domains. Keywords: AI Safety, Ontological Homomorphism, AAM-RSL, Hallucination Detection, R&D, Entropy, Epistemology.
ANDRII ARTSYBASHEV (Wed,) studied this question.
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