Measuring Semantic Coherence in AI and Knowledge Systems

Subtitle: Candidate Metrics for Meaning Stability, Contradiction, and Context Preservation

Author: Mark Tovey (Codex Resonance) Status: Draft v0.1 Date: 2026-05-25

Abstract

Semantic coherence is the preservation of meaning across systems, contexts, and time. This working paper frames semantic coherence as a measurable research problem for AI and knowledge systems, motivated by governance failures that arise when definitions drift, context collapses, provenance is lost, and policy intent is misapplied. It proposes a candidate metric structure—dimensions, observable signals, and evaluation pathways—without presenting a finished scoring system or disclosing proprietary methods. The intent is to support academic–industry discussion on how to detect meaning fragmentation, quantify contradiction and context loss, and establish temporal stability baselines for meaning-critical systems. The paper remains enterprise-safe and IP-aware: it avoids algorithms, schemas, scoring mechanics, or any Codex Kernel implementation detail.

Disclosure boundary: This paper intentionally avoids any scoring formulas, weighting schemes, thresholds, or implementation recipes.

1. Introduction

As AI-enabled systems become embedded in enterprise workflows, organisations increasingly rely on machine-generated representations: classifications, summaries, entity resolution, risk flags, recommendations, and policy interpretations. In these settings, technical evaluation often focuses on accuracy, calibration, or task utility.

Yet many high-impact failures do not originate in model error. They originate in meaning fragmentation: definitions shift across teams and tools, context is dropped as information moves, provenance is lost in aggregation, and policy intent diverges from operational use. These are semantic governance failures.

This paper proposes that semantic coherence should be treated as a measurable architecture property—analogous to reliability or security posture—rather than as a metaphor. The goal is to outline candidate dimensions and evaluation approaches suitable for research collaboration and enterprise evaluation.

2. Why coherence matters in AI governance

AI governance requires more than performance monitoring. It requires that the meaning of outputs remains interpretable, bounded, and accountable as systems evolve.

Semantic coherence matters because it underpins:

• Interpretability context: what an output means, for whom, under what assumptions.
• Auditability: reconstructing the meaning, evidence, and policy conditions at the time a decision was made.
• Accountability: assigning decision rights and stewardship for definitions and constraints.
• Safety in change: preventing silent drift when models, data sources, or taxonomies change.

If coherence cannot be evaluated, governance becomes reactive: organisations discover meaning loss only after consequence has been locked in.

3. The problem of meaning fragmentation

Meaning fragmentation occurs when a system’s representations become inconsistent across components, time, or organisational boundaries.

Common fragmentation pathways:

• Definition drift: the label remains, but its boundary conditions change.
• Taxonomy divergence: parallel taxonomies evolve without reconciliation.
• Context collapse: a statement is moved to a new use case without its interpretive constraints.
• Provenance erosion: evidence and lineage degrade across transformations.
• Policy/intent mismatch: rules are applied in contexts they were not authored for.
• Temporal inconsistency: outputs remain in force after authority or assumptions have changed.

These pathways suggest that coherence must be evaluated across interfaces and transformations, not only within a single model or dataset.

4. Candidate dimensions of semantic coherence

This paper proposes candidate dimensions that can be evaluated without reducing coherence to a single score.

4.1 Meaning stability

Whether key terms and categories retain consistent definitions across systems and time.

4.2 Context preservation

Whether outputs carry sufficient interpretability context (scope, assumptions, applicability constraints) into downstream use.

4.3 Consistency and contradiction

Whether the system produces mutually incompatible statements about the same entities under the same conditions.

4.4 Lineage and provenance integrity

Whether meaning and evidence remain traceable across transformations, aggregations, and decisions.

4.5 Policy alignment

Whether outputs and recommended actions remain bounded by relevant policy intent and authority constraints.

4.6 Temporal consistency

Whether meaning and governance conditions remain valid under time and change (versioning, effective dates, supersession).

These dimensions can be treated as a measurement matrix: each dimension can have multiple candidate indicators and evaluation methods.

5. Graph-constrained semantic similarity (research agenda)

A major source of coherence failure is treating “semantic similarity” as purely statistical similarity of text embeddings or labels. In meaning-critical systems, similarity must be constrained by explicit structure: entities, relationships, role context, and definitional boundaries.

This paper proposes graph-constrained semantic similarity as a research agenda: similarity assessment that is conditioned on (a) entity identity, (b) relationship context, (c) definitional scope, and (d) permissible interpretation boundaries.

Candidate evaluation questions:

• Under what relationship context are two descriptions considered equivalent?
• What definitional boundaries must remain invariant for similarity to be meaningful?
• How does similarity change across versions of a taxonomy or ontology?

This section intentionally does not propose algorithms or scoring mechanics; it identifies what must be controlled for similarity to be governance-relevant.

6. Contradiction detection (research agenda)

Contradiction in AI and knowledge systems is often treated as a purely logical property (“A and not-A”). In enterprise settings, contradiction is frequently contextual: statements may conflict only under specific scopes, time windows, authority conditions, or policy constraints.

Candidate contradiction classes:

• Definitional contradiction: competing definitions for the same term.
• State contradiction: incompatible facts about an entity within the same effective time.
• Policy contradiction: recommendations that violate stated constraints or authorities.
• Evidence contradiction: claims that cannot be supported by available provenance.

Measurement stance: contradiction detection should be evaluated as “governance signal quality” rather than as a universal truth oracle.

7. Temporal stability indices (research agenda)

Coherence is not static; it must persist through changes in data, models, taxonomies, policies, and organisational interpretation. This suggests a need for temporal stability indices that answer:

• How frequently do definitions change?
• How often do outputs become invalidated by context or authority changes?
• How stable are category boundaries across releases?

A temporal stability index should be able to distinguish healthy evolution (controlled, versioned, reviewable) from drift (silent, unowned, or untraceable).

This paper treats temporal stability as a measurement category and does not propose proprietary index formulas.

8. Lineage and provenance integrity (research agenda)

Lineage and provenance integrity concerns whether a consumer can reconstruct:

• What sources contributed to an output
• What transformations occurred
• What definitions and constraints were in force
• What human approvals or overrides applied

Candidate integrity indicators:

• Coverage: critical outputs have provenance links
• Completeness: provenance includes context, not only source IDs
• Continuity: lineage survives cross-system movement
• Reconstructability: an auditor can reproduce the meaning basis without privileged internal access

The intent is to define integrity expectations suitable for governance evaluation, not to disclose any implementation approach.

9. Policy alignment checks (research agenda)

Policy alignment is often discussed as “alignment with human values,” but enterprises require a narrower, auditable meaning: alignment with authored policy intent, authority constraints, and context-specific applicability.

Candidate alignment questions:

• Is the output in-scope for the policy and authority conditions?
• Are required constraints explicitly represented at the point of use?
• Are exceptions logged with justification and review path?
• Does policy versioning propagate into downstream decisions?

Policy alignment checks should be framed as governance instrumentation: they help organisations detect misapplication, not claim compliance.

10. Human review and correction

Human review is frequently treated as an expensive “manual step.” In coherence-critical systems, human oversight is a structural necessity because meaning is partly institutional and cannot be fully delegated.

Candidate oversight mechanisms (public, non-implementation):

• Definition ownership and review boards for key terms
• Change-control checkpoints for taxonomy/ontology updates
• Escalation paths when provenance is insufficient or contradictions arise
• Post-incident meaning reconstruction and correction

Measurement implication: coherence metrics must include operational signals of review efficacy (timeliness, resolution rates, recurrence of drift).

11. Research questions

1. What minimal set of coherence dimensions is sufficient for enterprise governance evaluation?
2. Which indicators best predict downstream failure due to meaning fragmentation?
3. How should context be represented so it remains portable across system boundaries?
4. How can contradiction signals be made actionable without producing excessive false positives?
5. What temporal stability baselines distinguish healthy evolution from uncontrolled drift?
6. What provenance integrity expectations are reasonable across regulated and non-regulated domains?
7. How can policy alignment checks remain auditable without becoming compliance claims?
8. What human oversight structures yield measurable coherence improvements?

12. Lightweight evaluation pathway

This paper proposes a lightweight, research-friendly evaluation pathway suitable for pilot studies:

1. Select a meaning-critical workflow (e.g., risk classification, eligibility, compliance-adjacent decision support).
2. Identify meaning anchors: key terms, categories, and policy constraints relied upon.
3. Map transformation boundaries: where information crosses tools, teams, or representations.
4. Define candidate indicators per dimension (stability, context, contradiction, provenance, policy alignment, temporal consistency).
5. Run a time-boxed baseline (e.g., 4–8 weeks) to measure drift events, contradiction incidents, provenance gaps, and misapplication exceptions.
6. Introduce one governance intervention (definition change control, provenance requirements, or policy versioning) and observe change.

The pathway is deliberately methodological; it does not specify algorithms, schemas, or proprietary instrumentation.

13. Limitations and ethics

Limitations:

• Coherence cannot be reduced safely to a single universal score; it is multi-dimensional and context-dependent.
• Some coherence signals require institutional context; purely technical evaluation will be incomplete.
• Measurement can be gamed if treated as a target; governance must remain accountable and reviewable.

Ethics considerations:

• Provenance capture must respect privacy, confidentiality, and legitimate access constraints.
• Coherence governance can concentrate power; decision rights must be explicit and contestable.
• Metrics should not be used to launder accountability (“the score says it’s fine”).

14. Conclusion

Semantic coherence—the preservation of meaning across systems, contexts, and time—should be treated as a measurable research problem in AI and knowledge systems. This paper outlines candidate dimensions and research agendas for graph-constrained similarity, contradiction detection, temporal stability indices, provenance integrity, policy alignment, and human review. The objective is to support academically credible collaboration and enterprise evaluation without overclaiming technical maturity or disclosing protected implementation.

15. Recommended citation

Tovey, M. (2026). Measuring Semantic Coherence in AI and Knowledge Systems: Candidate Metrics for Meaning Stability, Contradiction, and Context Preservation (Working Paper, v0.1). Codex Resonance. URL: https://codexresonance.com/

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