External coherence graph

The external coherence graph designates the mapping of public signals (sources, mentions, entities, relations, attributes) that frame how an entity is interpreted by AI systems in the open web. It allows identifying where the narrative is coherent, where it is contradictory, and where it is vulnerable to contamination, collision, or capture.

In interpretive governance, exogenous “truth” is not a single text. It is a graph: relations between sources, entities, and attributes that produce a dominant interpretation.

Definition

External coherence graph is the structured set linking:

the entity (brand, person, concept, organization);
external sources (articles, directories, wikis, profiles, citations, aggregators);
attributes (description, offering, positions, dates, categories, promises);
relations (belonging, filiation, synonymy, opposition, competition, homonymy).

The graph is called “coherence” graph when it allows measuring signal compatibility among themselves and with the endogenous canon. It reveals zones where AI risks reconstructing an unstable narrative.

Why this is critical in AI systems

The open web is the training environment: AI aligns on dominant external patterns.
Secondary sources weigh heavily: they often stabilize a default interpretation.
Conflict is invisible: weak contradictions produce interpretive debt and inertia.

What the graph allows detecting

External authority conflicts: incompatible strong sources.
Neighborhood contaminations: reframing by dominant clusters.
Entity collisions: homonymies, acronyms, confusions.
Invisibilization: endogenous canon absent from external activation paths.
Capture: dominant framing imposed by saturation and vocabulary.

Practical indicators (symptoms)

AI systems explain the entity based on external sources that contradict each other.
The internal canon is ignored in responses (interpretive invisibilization).
Attributes from a neighbor are projected onto the entity (contamination).
An internal correction does not change the external interpretation (inertia / remanence).

What the external coherence graph is not

It is not a list of backlinks. It concerns semantic relations and interpreted attributes.
It is not a classic SEO audit. The objective is interpretation stability.
It is not a single truth. It is a mapping of the forces that shape AI output.

Minimum rule (enforceable formulation)

Rule ECG-1: any entity that claims a stable interpretation in the open web must map and monitor its external coherence graph, by identifying dominant sources, contradictions, homonymies, and capture zones. Without an external graph, exogenous governance remains blind.

Example

Case: a brand is described differently on a directory, a profile, an article, and a wiki. AI systems produce a hybrid synthesis.

Diagnosis: incoherent external graph (contradictions + contamination + dominant secondary sources).

Expected correction: realign dominant sources, reinforce the internal canon, publish governed negations, and reduce confusion zones.

Corpus role and diagnostic use

In the corpus, External coherence graph belongs to the machine-readable layer of interpretive governance. It describes how meaning, routes, entities, exclusions, reading conditions or authority signals can be exposed in a form that machines can parse. The concept is not a promise that external systems will obey, cite, rank, recommend or correct the entity automatically.

The diagnostic use is architectural. It helps determine whether a system can identify what should be read first, which surfaces are canonical, which signals are supporting, which exclusions matter and which routes should not be collapsed together. Without this layer, a site may be readable by humans while still ambiguous to retrieval systems, answer engines or agents.

Failure pattern to detect

The main failure is artifact inflation. A file, graph, manifest or structured signal may be treated as if its existence alone created authority. In this corpus, machine-readable signals must remain tied to canon, source hierarchy, response conditions and evidence. They clarify the reading environment; they do not replace proof of fidelity.

Reading rule

Use this definition with canonical source, canonical surface, machine readability, reading conditions and documentary architecture. The term should help explain how a corpus becomes easier to read without pretending that external systems are bound by it.

External coherence graph