The Ontological Interface Hypothesis: A Triadic Framework for Consciousness, Biological Negentropy, and the Entropic Limits of Algorithmic AI

The rapid ascent of Large Language Models (LLMs) has empirically demonstrated that complex syntactic reasoning and semantic manipulation do not require subjective experience (qualia). This paper proposes the Ontological Interface Hypothesis (OIH), a triadic framework that decouples cognition, consciousness, and biological self-preservation. We argue that while cognition is an algorithmic, substrate-independent process susceptible to the Data Processing Inequality and entropic degradation (Model Collapse), consciousness and biological negentropy may involve non-algorithmic aspects of a more fundamental ontological layer. We model the thalamocortical complex not as an additional computational engine, but as a phase-synchronized biological interface connecting the algorithmic substrate to this deeper realm. Furthermore, we address standard functionalist counter-arguments, suggesting that artificial agents lacking such an interface are likely to remain prone to cognitive drift and physical degradation, functioning primarily as deterministic proxies of human intent rather than autonomous subjective entities.