Why Third-Party AI Evaluation Still Fails Without a Human-State Variable: Toward a Rival Audit Architecture for Human Consequence in AI Governance

This paper argues that third-party AI evaluation remains structurally incomplete because current evaluation systems still focus on model outputs, policy compliance, benchmark performance, and visible failures while under-representing the human-state and relational variables through which AI systems become socially consequential. The paper introduces the human-state variable and the relational variable as missing completion layers for contemporary third-party AI evaluation. It presents the Consciousness Civilization Framework (CCF) as a minimal audit architecture for representing state-sensitive and relation-sensitive transformation, using Ordered Energy (OE), Entropic Energy (EE), and Relational Energy (RE) as evaluative variables, and VCE, CRI, and CFI as audit-relevant indices. The paper further proposes a rival audit architecture for human consequence in AI governance, including state-unit fixation, comparison conditions, observation windows, degradation thresholds, data-layer separation, submission architecture, cross-lab reproducibility, replayable audit schemas, and enforcement triggers that can affect deployment status, procurement eligibility, recertification, and post-deployment escalation logic. The paper positions CCF as the missing representational layer required for consequence-aware AI evaluation and frames CAIS / Sal-Meter as part of the implementation pathway once human consequence becomes a real audit object. This work is released as a Public Draft. It is intended as a governance-facing conceptual paper, a pressure document against the limits of current AI evaluation regimes, and a foundation for subsequent validation, measurement, and implementation work across the broader CCF / CAIS / Sal-Meter architecture. Official public hub: https://salpida.foundation/Public implementation index: https://github.com/salpida-foundation