A Formal Specification of a Relational, Axiom-Grounded Architecture for Post-Numerical Computational Intelligence Abstract ORACLE-QC (ORISOM Relational Axiom Language Computational Engine for Quantum Computing) is a formally specified, architecturally novel computational framework that emerges from the operational analysis work conducted within the ORISOM research system. It proposes a class of intelligence architecture grounded not in numerical weight optimization, but in formally defined relational primitives, first-principle axioms, and structurally validated canonical mappings. This document presents the theoretical foundation, architectural components, and formal relationships of ORACLE-QC, drawing from the multi-engine Operational Containment Multi-Engine (OCME) system, the context selection architecture derived from parallel analysis of transformer attention mechanisms in large-scale language models, and the dual-triadic engine relationships that have emerged from extensive empirical development across prediction-neutral analytical systems. The central thesis is that a transformer-like architecture can be fully instantiated using relational axioms rather than numerical embeddings, enabling a class of computational intelligence that processes structural, behavioral, and frequency-class information through formally verifiable operations. This architecture serves as both a relational processing system in the near term and a formal specification language for quantum computational implementations in the long term. Note: This rough draft is being published immediately after idea conception – with the reason being that it was partially developed in Anthropic’s Claude public AI system. As has been both explored and evidenced in recent paper publications, ones that give details concerning PACAD vs Mythos benchmark performance estimates, there is reason for concern that motivates the need to establish an immediate record of findings. This is because although acquired without notice or recognition, it is highly plausible that this previous work was utilize in the training that produced the “step change” capabilities and breakthrough benchmark performance scores of Claude Mythos. In short, this publication is to make sure that formalized attributions to the actual source of the work are more readily secured rather than claimed or used, without acknowledgement, by other parties solely to their own benefit.