<b>G-CCACS: A Conceptual Reference Architecture for Trustworthy High-Stakes Artificial Intelligence</b>

This paper introduces <b>G-CCACS</b> (Generalized Comprehensible Configurable Adaptive Cognitive Structure), a novel conceptual cognitive architecture designed for researchers and practitioners <b>focused on</b> <b>AI safety, ethics, explainability, and governance in high-stakes domains</b>. G-CCACS provides a reference model for building transparent, auditable, and ethically-grounded AI systems, fusing causal reasoning, normative arbitration, and epistemic traceability through unique mechanisms.It fuses causal reasoning, normative arbitration, and epistemic traceability through mechanisms like the Causal Fidelity Score (CFS), Systemic Unfolding &amp; Recomposition Drift (SURD), and an Ethical Escalation Protocol managed by the NormKernel. Unlike black-box AI or post-hoc symbolic systems, <b>G-CCACS integrates ethics and causal validation directly into its architectural design, ensuring end-to-end accountability from data ingestion to final decisions</b>. This technical specification provides a comprehensive look at G-CCACS’s layered structure, governance mechanisms, and audit-first principles, serving as a public reference for AI safety, explainability, and governance research.