Governed Intelligence: A Spectral Framework for Verifiable AI Accords and Structural Security

We present a governed intelligence architecture that separates an AI agent’s proposal engine (Ghost) from a hard execution gate (Hermes) to enforce structural accords and security invariants. The system’s safety is verified through a novel spectral structural operator K(H) = λ1:k L(H) that extracts the leading eigenvalues of the normalized graph Laplacian of the embedding space. This operator converts representational collapse from a metaphor into a differentiable, measurable quantity. We provebthe operator’s utility through two experiments: (1) a control-theoretic observer-augmented safety filter (VECTOR) that achieves zero false negatives under sensor degradation, and (2) a reinforcement learning safety grid (SafetyGrid DSE) where a spectral-trajectory gate reduces catastrophic violations by 98% compared to payload-only filters. The framework is formalized as a Whitebox AI safety paradigm, where internal state is instrumented and structural invariants are continuously monitored.