This paper introduces the Dynamic Coherence Window (DCW) framework, a structural viability approach to AI safety that monitors system coherence in real time rather than filtering outputs after generation. The framework defines a five-dimensional defect vector tracking structural degradation, emergent thresholds derived from transverse coupling, and two state variables, the coherence margin m and stability parameter Λ, that together determine system phase. We extend the framework with a Cognitive Immune System (CIS) that evaluates the coherence impact of new learning before integration, providing four decision paths: accept, reject, quarantine, and reframe. The reframe mechanism introduces human coherence bridging, analogous to regulatory T-cell tolerance in biological immunity. Empirical validation on 21 test cases demonstrates that the framework correctly distinguishes local coherence from transverse viability, with aggregated incoherent content yielding Λ = 0.44 and m = −1.17 despite acceptable local metrics. A working implementation (CMCI v8.1.4) is deployed as a production service with REST API and real-time dashboard. Four patents related to this work are pending with the Canadian Intellectual Property Office.
Christian St-Louis (2026) studied this question.