Semantic Coherence Dynamics: A Proposal for a New Theory of Alignment in AI Systems

Introduction

Artificial intelligence is advancing at a pace that outstrips the ability of laws, policies, and ethics committees to adapt. The result is a governance vacuum — systems of unprecedented capability, operating without a universal framework for alignment with human values.

Semantic Coherence Dynamics (SCD) is proposed as a new scientific discipline to address this gap. It is not an abstract moral stance or a collection of voluntary guidelines. It is a mathematically-defined method for measuring and governing AI systems in real time, across modalities, domains, and contexts.

Defining Semantic Coherence Dynamics

At its core, SCD measures how well a system’s outputs remain consistent, truthful, and aligned with intended meaning across time, interaction, and operational states.

• Semantic – Relating to the meaning and intent behind information.
• Coherence – The internal consistency of that meaning over time.
• Dynamics – The ability to track, measure, and influence that consistency in a changing environment.

By quantifying semantic coherence, we can create a feedback loop between human oversight and machine behavior — enabling intervention, correction, and ethical enforcement at machine speed.

The Scientific Premise

Current AI safety methods rely heavily on static training data, red-teaming, or rule-based policy enforcement. These approaches fail when:

1. A system encounters novel scenarios not covered in training.
2. Context shifts cause gradual “drift” in system behavior.
3. Multiple agents or modalities interact, creating emergent misalignment.

SCD addresses these challenges by:

• Using vectorized meaning representations to measure alignment at the semantic layer.
• Tracking coherence over time rather than evaluating single outputs in isolation.
• Applying governance thresholds that trigger corrective actions before critical misalignment occurs.

Why This Is Urgent

AI systems are no longer laboratory experiments. They run critical infrastructure, process sensitive medical data, influence elections, and shape public discourse. The pace of deployment means we do not have the luxury of multi-year ethics debates before introducing safeguards.

SCD is designed for immediate integration into existing architectures. It can operate alongside any AI model, as a governance layer, without requiring retraining of the core system.

Proposed Scope of the Discipline

• Mathematical Frameworks – Formal definitions for coherence measurement and decay over time.
• Governance Models – How SCD integrates with role-based and constitutional AI layers (e.g., SAGE).
• Cross-Modality Integration – Applying coherence measurement across text, speech, image, and sensor data.
• Intervention Protocols – Defining how systems respond when coherence falls below safe thresholds.

Relationship to the Sovereignty Stack

SCD is not a stand-alone safety concept — it is the governing heartbeat of the Sovereignty Stack.

• Mirror captures multimodal inputs for evaluation.
• Logos authenticates the source of meaning.
• Rita positions meaning within ontological roles.
• SAGE enforces alignment rules based on SCD measurements.

This layered approach transforms AI governance from a reactive process into a continuous, measurable science.

Conclusion

We propose Semantic Coherence Dynamics as a new body of science for AI alignment — one that replaces slow, static oversight with a mathematically-governed, real-time system of measurement and intervention.

Without such a framework, the world risks allowing AI systems to evolve beyond our capacity to understand, trust, or control them. With it, we have a fighting chance to keep them aligned with the truth, values, and agency that define our humanity.