Executive Summary

Structural Coherence as the Missing Axis in AI Evaluation

A framework for evaluating AI systems as evolving structures rather than static outputs.

Contemporary evaluation frameworks largely treat AI systems as static artifacts. Frontier models instead exhibit context-sensitive adaptation, trajectory-dependent behavior, and pressure-induced drift.

Key Research Outcomes

  • Composite Measurement: We operationalize these principles through a composite measurement framework that quantifies cross-context representational stability and transition volatility across structured cognitive dimensions, resulting in a composite stability index.
  • Stability as Metric: We formalize representational stability under uncertainty as a measurable axis of structural integrity.
  • Trajectory Tracking: We model accountability as trajectory-sensitive, capturing recovery and degradation dynamics rather than point-in-time snapshots.
  • Unified Annotation Schema: A structured annotation methodology for quantifying cross-context representational stability and transition dynamics.

This research introduces a structural framework for evaluating intelligent systems in regimes where behavior-based metrics, scalar scores, and frozen audits prove insufficient.

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