The Incompleteness of Stochastic Intelligence: Why Coherence, Not Probability, Is the Substrate of Meaning

CODES Framework Abstract This paper challenges the foundational assumption of modern artificial intelligence: that intelligence can emerge from probabilistic generation alone. We argue that stochastic systems—no matter how fluent—lack structural coherence, ontological grounding, and lawful emission logic. The result is a class of models that can simulate truth without ever holding it. Drawing from the CODES framework and Resonance Intelligence Core (RIC), we present a substrate-level alternative based on deterministic coherence enforcement. Using Phase Alignment Score (PAS), chirality-anchored logic, and emission gating, we formalize a post-stochastic substrate for intelligence that replaces guessing with structure, and performance with principle.