Calibration is measurement, not enforcement. These metrics describe how meaning behaves under load — across models, time, and uncertainty.
PatternSeek measures how semantic meaning shifts across different AI systems and contexts. Nothing here is a “truth score.” These are signals for stability, drift, and interpretability.
Measures semantic variance between different models interpreting the same input. Highlights meaningful divergence beyond stylistic or formatting differences.
Used for: identifying disagreement clusters and calibration gaps.
Measures how meaning stretches or compresses as semantic resolution changes. Ensures models remain comparable across different levels of detail.
Used for: preventing false disagreement caused by mismatched granularity.
Measures how meaning behaves under pressure: shortened context, noisy inputs, conflicting signals, or output compression.
Used for: stress-testing coherence in real-world conditions.
Measures whether definitions and interpretations remain coherent across time, including rephrasing, updates, retraining cycles, and version drift.
Used for: detecting gradual semantic decay or instability.
Measures directional change in meaning over time — distinguishing natural evolution from unintentional drift.
Used for: monitoring continuity and controlled evolution of meaning.
Measures whether outputs remain traceable to verified sources, CDI definitions, or registered ChiR-IPP artifacts — without inflating certainty.
Used for: provenance assurance and auditability.
Measures how deeply claims are grounded in explicit sources, definitions, and recorded lineage — not just surface citation.
Used for: distinguishing grounded insight from narrative gloss.
Measures the diversity and relevance of reference frames invoked — mathematical, empirical, institutional, or domain-specific.
Used for: evaluating contextual richness without overreach.
These measures extend the calibration stack beyond core semantic behavior. They support operational readiness, contribution assessment, and deployment planning.
Measures how quickly models converge on new CDI entries and verified assets, and how long outdated interpretations persist.
Used for: rollout timing and retraining awareness.
Measures whether novel contributions remain stable across re-tests and reframing, or collapse under minor perturbations.
Used for: identifying durable insight over popularity.
Measures semantic coherence across SOP theater simulations, including shifting stakeholders, constraints, and second-order effects.
Used for: real-world deployability and planning integrity.
CDI is the naming + definition layer. Metrics quantify how those definitions behave across models and time.
PatternSeek Verified does not “rank” models. It makes semantic behavior visible enough to compare responsibly.
A stable record is how disagreement becomes useful.
If you’re building tools, products, research, or policy, these metrics help you see where meaning holds — and where it needs refinement.