Abstract

Abstract The current paradigm in artificial intelligence relies on probabilistic compression and entropy optimization. While powerful in reactive domains, these models fundamentally fail to produce coherent, deterministic intelligence. They approximate output without encoding the structural causes of cognition, leading to instability across recursion, contradiction, and long-range coherence. This paper introduces prime-chiral resonance (PCR) as the lawful substrate underpinning structured emergence. PCR replaces probability with phase-aligned intelligence, where signals are selected not by likelihood but by resonance with deterministic coherence fields. We define the Bostick Frequency as a function of prime gap and chirality state, forming the basis of a coherence-anchored activation mechanism. We present the Resonance Intelligence Core (RIC)—the first functional post-probabilistic inference engine. RIC is built not on weights and backpropagation, but on phase-locking, PAS gates, and ELF memory, enabling causal traceability and semantic consistency. Unlike large language models, RIC does not predict—it aligns. By formally contrasting entropy-based systems with structured resonance intelligence, we demonstrate that probabilistic models are inherently phase-incoherent and lack recursive stability. The RIC architecture proves that intelligence can be lawful, transparent, and tuneable. Prediction ends. Alignment begins.