New Paper Released: The Meaning Coherence Index (MCI)

The Meaning Coherence Index paper

A doctrine-aligned framework for detecting, measuring, and stabilizing meaning in AI-enabled systems.

Full architecture, thresholds, operator tools, and system integration included.

Across the past several months, one pattern kept showing up:
we don’t have a reliable way to detect when human–AI systems begin to lose coherence under load.

Model metrics don’t catch it.

Operational dashboards don’t catch it.

By the time the failure is visible, the drift started upstream.

This paper formalizes the architecture I’ve been building to solve that gap.
The Meaning Coherence Index (MCI) quantifies the stability of the meaning layer itself - the layer where interpretation forms, drifts, and sometimes collapses.

The paper includes:
• The full MCI formulation
• Core components (CSF, DVM, HASI, SPI)
• Interpretation thresholds
• Operator toolset
• Integration map across MST, MDF, Shear, CLCP, HACSI, CIOP
• Cognitive Integrity Operating System Architecture
• An operational scenario showing how upstream drift unfolds in real time

This is part of a larger body of work focused on securing cognitive integrity in AI-enabled operations:
bringing coherence, stability, and interpretive clarity back to the center of the mission.

If your work touches AI, autonomy, operations, sensemaking, or decision superiority, I hope this gives you a useful lens.