A Safety-Oriented Development Path for QFG-Based Recursive Interpretability and Cross-Resolution Grounding

This work presents a safety-oriented conceptual framework for the development of QFG-based recursive interpretability systems. The framework integrates three core components: recursive interpretability, cross-resolution contradiction detection, and external grounding. The central claim is that interpretability alone is insufficient for AI safety unless it is structurally enforced across multiple levels of representation and anchored to external reality. The paper formalises contradiction detection across resolution scales using projection operators and introduces a mathematical contradiction functional as a safety signal. A staged development pathway is proposed, prioritising containment, auditability, and external validation before any form of recursive self-modification is permitted. The work explicitly avoids providing an implementation blueprint and instead defines safety conditions under which such systems might be explored responsibly. This paper serves as a conceptual and mathematical foundation for safe interpretability development within a Quantum Fractal Geometry (QFG) framework, with explicit emphasis on benefit-to-all outcomes and prevention of deceptive or self-justifying system behaviour.