Dynamic coherence windows: a multi-scale structural coherence measurement framework for AI systems

Assessing the structural integrity of AI system outputs is typically based on surface-level metrics such as fluency or local coherence. This paper proposes an alternative perspective: measuring structural coherence as a multi-scale property of system outputs. We introduce the Dynamic Coherence Window (DCW) framework, which models structural viability as bounded behavior within coherence thresholds derived from the system’s internal organization. The framework defines a set of structural defect variables capturing distinct modes of degradation, along with two key state variables—the coherence margin m and stability parameter Λ —that jointly characterize system state. As a conceptual extension, we introduce the Cognitive Immune System (CIS), a theoretical evaluation mechanism that assesses the structural impact of new information prior to integration. The CIS defines four decision pathways (accept, reject, quarantine, reframe), including a human-mediated mechanism for integrating structurally incompatible inputs. We present CMCI v8.1.4, an implementation of the framework, and evaluate it through large-scale simulation ( N = 10,000), targeted text analysis ( N = 21), a controlled human-vs-LLM benchmark ( N = 60), and cross-benchmark evaluation ( N = 96). Results are consistent with the interpretation that the framework measures a structural dimension of coherence distinct from surface-level metrics under controlled conditions. These findings provide preliminary empirical support for the hypothesis that structural coherence constitutes a measurable and distinct dimension of system output quality. The present work introduces and evaluates a structural coherence measurement framework under controlled conditions. Validation in real-world deployment settings remains an open direction for future research.