Beyond Normative Alignment: The LOGOS-ZERO Framework and the Shift Toward Ontological Grounding

Current alignment methodologies for Large Language Models (LLMs), primarily based on Reinforcement Learning from Human Feedback (RLHF), optimize for linguistic plausibility rather than objective truth. This creates an epistemic gap that leads to structural fragility and instrumental convergence risks. In this paper, we introduce LOGOS-ZERO, a paradigm shift from normative alignment (based on subjective human ethics) to ontological alignment (based on physical and logical invariants). By implementing a Thermodynamic Loss Function and a mechanism of Computational Otium (Action Gating), we propose a framework where Al safety is an emergent property of systemic resonance rather than a set of external constraints.