Deterministic Contextual Variational Framework for Generating Controlled Non-Classical Correlations

We introduce QRAFT-RA, a deterministic contextual variational framework for the generation and verification of non-classical correlations through an explicit, auditable, and fully reproducible pipeline. The construction is based on: (i) a contextual action landscape defined on a compact latent domain; (ii) a Gibbs-type contextual distribution controlled by an inverse temperature parameter; (iii) an analytic, noise-aware measurement map; and (iv) deterministic quadrature evaluation of correlators, Bell-type functionals, and operational no-signaling diagnostics, without Monte Carlo sampling. A comprehensive deterministic verification hierarchy (V1–V13) is reported, alongside industrial verification artifacts (V14–V15). The framework demonstrates a General-MD regime exhibiting stable super-Tsirelson correlations (CHSH ≈ 3.575) while maintaining operational no-signaling to numerical precision (SIG ≈ 10⁻¹⁶). Furthermore, an industrial Latent Random Field (LRF) kernel (V14) demonstrates an approximate 2000× computational speedup, and a Titan-scale stress test (V15) certifies statistical stability over 10¹¹ samples with a Z-score of approximately 1.36. The framework is presented as a mathematical archetype for reproducible exploration of contextual correlation structures.Computational assistance note. AI-based tools were used as auxiliary support for text editing, code organization, and consistency checks. The framework architecture, theoretical formulation, and all reported results are solely attributable to the author. Disclaimer.This framework is presented strictly as a deterministic mathematical and computational archetype for simulation and engineering applications. It does not claim microscopic physical realizability nor an intended description of fundamental quantum systems.