Measuring Constitutional Fidelity: EOS Diagnostics for Specification-Anchored AI

This paper proposes the Emergent Order Systems (EOS) framework and its Coherence Triad (C-Index, L-Index, R-Index) as diagnostic infrastructure for evaluating constitutional fidelity in principle-governed AI systems. Constitutional AI (Bai et al., 2022) provides a methodology for instilling behavioral principles during training; what it does not provide is runtime measurement of whether those principles are holding under deployment conditions. EOS addresses this gap by mapping each Triad index to a distinct class of alignment failure: C-Index for specification-to-output drift, L-Index for boundary transfer degradation in critique chains and oversight protocols, and R-Index for proximity to constitutional regime collapse. Two proof-of-concept demonstrations apply the Triad to a CAI self-critique chain and a scalable oversight debate protocol, revealing structural dynamics — particularly judge-boundary bottlenecks and asymmetric transfer quality — invisible to existing output-level metrics. Empirical grounding is provided by the Companion and Reflex Core datasets (72-hour stateless agent behavioral fidelity) and prior LLM phase transition validation. The paper concludes with five testable predictions and application-specific falsification criteria. Part of the EOS/ECR framework family.