Omni-Resonance Engine (ORE): Phase-Locking Kuramoto Dynamics in Sub-Billion Parameter Models for High-Fidelity COGNITIVE Reasoning

Abstract

The prevailing paradigm in natural language processing relies heavily on aggressively scaling parameter counts in Transformer architectures to achieve emergent reasoning capabilities. This brute-force scaling inherently limits the deployment of advanced AI in privacy-critical, resource-constrained edge environments such as hospitals, law firms, and financial institutions. In this paper, we introduce the Omni-Resonance Engine (ORE), an 892-Million parameter foundational dense model that defies conventional scaling laws. ORE replaces standard multi-head attention with a Fused Kuramoto-Attention Modulator (KAM) coupled with a SwiGLU Multi-Layer Perceptron. By modeling 1,280 cognitive agents (divided into 32 factions) as coupled oscillators governed by Ordinary Differential Equations (ODEs) and a Liquid Time-Constant (LTC), ORE forces mathematical consensus before token emission, drastically mitigating hallucinations. Pre-trained from scratch on billions of tokens and iteratively refined through a novel Continuous Learning loop via automated knowledge distillation, ORE achieves expert-level logical, scientific, and enterprise reasoning capabilities natively on edge hardware without compromising strict proprietary privacy.