Visual schema
From term to framework
Definitions stabilize the vocabulary before doctrine, frameworks, and operational usage.
Canonical term
Name without ambiguity.
Scope
Delimit what the term covers.
Doctrine
Connect the term to the doctrinal frame.
Framework
Make it applicable inside a system.
Usage
Mobilize it in posts, cases, and audits.
Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Public AI manifest
/ai-manifest.json
Structured inventory of the surfaces, registries, and modules that extend the canonical entrypoint.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
- Governs
- Public identity, roles, and attributes that must not drift.
- Bounds
- Extrapolations, entity collisions, and abusive requalification.
Does not guarantee: A canonical surface reduces ambiguity; it does not guarantee faithful restitution on its own.
Complementary artifacts (2)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Identity lock
/identity.json
Identity file that bounds critical attributes and reduces biographical or professional collisions.
Claims registry
/claims.json
Registry of published claims, their scope, and their declarative status.
Before using the full registry
When the right definition to read first is unclear, start with Start here. The guided paths separate authority, evidence, visibility, entity stability, RAG, agentic execution, and memory correction before sending the reader into this complete registry.
Definitions and canonical concepts
This page serves as a public registry of canonical definitions used in the interpretive governance doctrine developed by Gautier Dorval.
It lists the primary conceptual references that govern term usage on this site and that aim to frame machine interpretation when they are encountered.
This registry constitutes neither an operational method nor a promise of results. It exists to reduce ambiguity by declaring stable conceptual perimeters.
The canonical entity graph is published here: /entity-graph.jsonld
Link hierarchy for this registry
This registry is a layered map of canonical concepts, not a flat list of equivalent links. The first pass should identify the page that owns the term. The second pass can open supporting definitions, frameworks or service pages.
Start here
- Core doctrine: Interpretive governance, Interpretive risk, Interpretive legitimacy and Answer legitimacy.
- Authority and response: Source hierarchy, Authority boundary, Response conditions and Legitimate non-response.
- Evidence and correction: Proof of fidelity, Interpretation trace, Canon-output gap and Correction budget.
- Market bridge: LLM visibility, Citability, Recommendability and AI search monitoring.
Supporting routes
Reading rule
Use definitions as the canonical surfaces for terms. Hubs, services, articles and frameworks should support them without silently absorbing their definition intent.
Quick navigation
- Observable phenomena (field)
- Authority, limits, and non-response
- Evidence, audit, and observability
- Governances and architecture
- Application contexts
Observable phenomena (field)
- Interpretive invisibilization
- Interpretive collision
- Interpretive capture
- Interpretive inertia
- State drift
- Interpretive smoothing
- Interpretive remanence
- Citation persistence
- Neighborhood contamination
- Interpretive trail
Authority, limits, and non-response
- Authority boundary
- Surviving authority
- Authority Governance (Layer 3)
- Authority conflict
- Legitimate non-response
- Canonical silence
- Governed negation
- Response conditions
- Interpretive hallucination
- Interpretability perimeter
Evidence, audit, and observability
- Interpretive evidence
- Reconstructable evidence
- Proof of fidelity
- Interpretation trace
- Canon-output gap
- Interpretation integrity audit
- Interpretive observability
- Semantic calibration
- Compliance drift
- Interpretive debt
- Interpretive sustainability
- Version power
- Canonical fragility
Governances and architecture
- Interpretive governance
- Endogenous governance
- Distributed interpretive authority governance
- Exogenous governance
- External coherence graph
- Memory governance
- SSA-E + A2 + Dual Web
- Semantic compression
- AI disambiguation
- Interpretive SEO
Application contexts
- Agentic
- Non-agentic systems
- Post-semantics (thinking & reasoning) vs interpretive governance
- Interpretive SEO vs Entity SEO vs GEO vs AEO
Market and bridge vocabulary
Some terms now circulate as easier entry labels for the same family of problems. On this site, they are captured explicitly and then redirected toward the doctrinal canon.
- Semantic integrity: readable entry label for stability of meaning under interpretation.
- Semantic accountability: bridge term for assumable meaning under proof, authority, and response conditions.
- LLM visibility: broad label requalified through structural visibility, citability, and recommendability.
- Delegated meaning: bridge expression for reconstructed meaning that no longer remains directly anchored to canon.
- Interpretive evidence: broader evidentiary family for how meaning was formed, bounded, and challenged.
- Reconstructable evidence: evidence packaged well enough for third-party reconstruction and later review.
Recommended clarifications:
- Semantic integrity vs interpretation integrity
- LLM visibility vs citability vs recommendability
- Delegated meaning vs silent delegation of authority
- Interpretive evidence vs proof of fidelity
Recently published definitions
- Citation persistence
- Surviving authority
- Interpretive evidence
- Reconstructable evidence
- Proof of fidelity
- Interpretation trace
- Canon-output gap
- Interpretation integrity audit
Recently captured risk, chain, and reporting vocabulary
These terms are now also captured through service-facing expertise pages:
On this site, they remain operational entry points. They redistribute toward Interpretive risk, Interpretive governance for AI agents, the Evidence layer, and Proof of fidelity.
Phase 1 canonical ownership layer
These definition pages are now primary SERP ownership surfaces for strategic terms in the interpretive governance lexicon. They should receive descriptive internal links from hubs, categories, glossary pages, articles and external evidence when available.
- Interpretive risk
- Interpretive legitimacy
- Answer legitimacy
- Source hierarchy
- Silent delegation of authority
- Durable interpretive presence
- Canonical surface
Their role is to make one query, one concept and one primary URL explicit.
Phase 2 canonical ownership layer: authority, refusal, and coherence controls
These definition pages are now primary SERP ownership surfaces for the second layer of the interpretive governance lexicon. They govern how authority is ordered, where interpretation stops, when inference is prohibited, and how smooth answers can hide illegitimacy.
- Interpretive authority
- Interpretive perimeter
- Authority ordering
- Authority conflict
- Governed negation
- Mandatory silence
- Inference prohibition
- Unauthorized synthesis
- Manufactured coherence
- Surface coherence
Their role is to prevent Google, LLMs and internal agents from treating plausible synthesis as governed interpretation.
Phase 3 canonical layer: evidence, audit, trace, and measurement
These pages are now the primary canonical surfaces for the proof and observability side of the interpretive governance lexicon.
- Interpretive evidence
- Reconstructable evidence
- Proof of fidelity
- Interpretation trace
- Canon-output gap
- Interpretive observability
- Interpretive auditability
- Evidence layer
- Q-Ledger
- Q-Metrics
Their role is to prevent evidence, metrics, citations, and audits from being treated as interchangeable. Observation records what happened. Metrics summarize observations. Trace explains the path. Reconstructability preserves the case. Proof of fidelity tests canonical containment. Auditability makes the whole chain contestable.
Phase 4: canon, corpus, and machine readability
The phase 4 definition layer adds canonical ownership surfaces for the documentary architecture that governs machine interpretation:
- Canonical source
- Machine readability
- Machine-first canon
- Machine-first artifacts
- Documentary architecture
- Reading conditions
- AI manifest
- AI governance JSON
- Entity graph
- Global exclusions
- Non-inference regime
This layer connects definitions, public artifacts, entity data, exclusions, and sitemaps into a single interpretive structure.
Phase 5: AI visibility, citability, recommendability, and market bridge terms
The phase 5 definition layer creates primary SERP ownership surfaces for market-facing AI visibility vocabulary. These terms are intentionally captured because readers search for them before they search for interpretive governance.
- LLM visibility
- Citability
- Recommendability
- AI search monitoring
- GEO metrics
- AI citation tracking
- AI brand representation
- Brand visibility in ChatGPT
- Generative engine optimization
- AI search optimization
- AI answer audit
- Semantic integrity
- Semantic accountability
- Delegated meaning
This layer must be used as a bridge. Visibility, monitoring, optimization, citation, and recommendation are not interchangeable. Each term points back to canon, evidence, source hierarchy, machine readability, and answer legitimacy.
Phase 6: semantic architecture, entity stability, and drift control
The phase 6 definition layer creates primary SERP ownership surfaces for the semantic stability layer of interpretive governance. These terms explain how entities, doctrines, brands, products, and concepts remain separable and correctly framed across AI systems.
- Semantic architecture
- Entity disambiguation
- Entity collision
- Semantic neighborhood
- Semantic contamination
- Framing stability
- Cross-system coherence
- Interpretive drift
This layer must be used before amplification. If the entity graph, semantic neighborhood, and framing are unstable, more content or more links can strengthen the wrong interpretation.
Phase 7: RAG, retrieval, documentary chain, and correction control
The phase 7 definition layer creates primary SERP ownership surfaces for the part of interpretive governance where retrieved documents become answer material. These terms prevent RAG, citations, and retrieval pipelines from being mistaken for answer legitimacy.
- RAG governance
- Retrieval control
- Documentary chain
- Source admission
- Corpus admissibility
- Retrieval provenance
- Chunk authority
- Response web
- Correction budget
- Resorption
This layer must be used whenever a system claims that retrieval, citation, search relevance, or a larger corpus is enough to govern the answer. It connects source admission, retrieval provenance, chunk boundaries, proof of fidelity, answer legitimacy, version power, and correction resorption into one auditable chain.
Phase 8 canonical ownership layer: agentic execution and transactional control
These definition pages are now primary SERP ownership surfaces for the agentic execution layer of the interpretive governance lexicon. They govern what changes when a response becomes a tool call, a delegated action, a multi-agent handoff, a transactional update, or an externally consequential execution.
- Agentic
- Non-agentic systems
- Agentic risk
- Multi-agent chains
- Delegated action
- Tool-mediated authority
- Execution boundary
- Transactional coherence
- Cross-layer transactional coherence
- Agentic response conditions
Their role is to prevent agents, search engines, and LLMs from treating capability, tool access, or user intent as sufficient authority for execution.
Phase 9 canonical ownership layer: memory, persistence, remanence, and correction
These definition pages are now primary SERP ownership surfaces for the memory and persistence layer of the interpretive governance lexicon. They govern what survives after an answer, correction, retrieval event, or agentic action.
- Memory governance
- Agentic memory
- Memory object
- Persistent assumptions
- Controlled forgetting
- Stale-state handling
- Surviving authority
- Interpretive remanence
- Interpretive inertia
- Version power
- State drift
- Correction budget
- Resorption
- Correction resorption
Their role is to prevent memory, persistence, old citations, surviving authority, stale state, and residual interpretations from being treated as current, authorized, or corrected merely because they remain available.
Phase 10 canonical ownership layer: inference, arbitration and interpretive error space
These definition pages are now primary SERP ownership surfaces for the inference-control layer of the interpretive governance lexicon. They govern what happens when a system completes gaps, chooses between sources, hides uncertainty, or turns plausible meaning into a final answer.
- Interpretive error space
- Free inference
- Default inference
- Arbitration
- Indeterminacy
- Interpretive fidelity
Their role is to prevent search engines, LLMs and agents from treating plausible completion, semantic proximity or smooth synthesis as legitimate interpretation.
Phase 12 canonical ownership layer: debt, maintenance, and deprecation
These definition pages are now primary SERP ownership surfaces for the maintenance layer of the interpretive governance lexicon. They govern what happens after canonical publication: how semantic ambiguity accumulates, how a canon remains current, how obsolete surfaces lose authority, and how corrections move from publication to resorption.
- Semantic debt
- Canon maintenance
- Interpretive maintenance
- Maintenance burden
- Correction backlog
- Deprecation discipline
- Canonical refresh cycle
- Obsolescence control
The routing rule is direct: do not treat publication, availability, recency metadata or volume as durable authority. Authority must be maintained, deprecated, corrected and resorbed through declared processes.
Phase 13 routing layer: service audits and market entry points
Phase 13 adds a service-facing routing layer for audit demand: LLM visibility audit, AI answer audit, AI brand representation audit, representation gap audit, AI citation analysis, AI source mapping, comparative audits, drift detection, pre-launch semantic analysis, interpretive risk assessment, and independent reporting.
These terms should be treated as market entry points. They capture real demand, then route the work toward canon, source hierarchy, evidence, answer legitimacy, auditability, and correction resorption.
Phase 13: market audit definitions
Phase 13 adds market-facing audit definitions so high-demand search labels resolve to canonical routes instead of floating as loose SEO terms.
- LLM visibility audit
- AI visibility audit
- AI brand representation audit
- AI citation tracking audit
- Citability audit
- Recommendability audit
- Generative engine optimization audit
- AI search optimization audit
- Brand visibility in ChatGPT audit
- AI answer audit
These definitions should be read with Services, audits, and market bridge vocabulary and AI visibility audits.
Phase 14 SERP ownership routing
This registry owns exact definition intent. When a query asks what a concept means, the preferred route is a canonical definition page, not a service page, glossary family, category archive, or article.
Use the SERP ownership map to distinguish definition intent from audit intent: LLM visibility is the definition; LLM visibility audit is the service route. Citability is the concept; Citability audit is the audit route.
Internal routes to reinforce
These links keep definitions surfaces visible when they support disambiguation, evidence, service routing, or canonical reading, without making them depend only on template-generated listings.
- Defensible inference · Defined authority · Inference boundary · Inferred authority · Interpretive SEO vs Entity SEO vs GEO vs AEO · Semantic calibration
Citation readiness vocabulary
The citation readiness cluster adds six supporting definitions for the practical layer between SEO visibility and interpretive governance: citation fidelity, retrieval without citation, preview control, AI-ready structure, discovery surface and machine-first routing.
These terms should be routed back to AI citation readiness when the question is upstream, and to AI citation tracking when the question is observational.
LLM perception drift and AI perception drift cluster
This hub now includes a dedicated path for LLM perception drift, connecting the emerging market term with the site’s canonical concepts.
- AI perception drift : the main term for generative representation variation.
- LLM perception drift : the more technical variant centered on large language models.
- AI perception stability : the inverse target, centered on fidelity and convergence.
- AI perception baseline : the initial state required to measure drift.
- Cross-model drift : representation divergence across several models.
- Category drift : wrong classification of an entity in a market, role, or neighborhood.
- Recommendability drift : variation in the propensity of AI systems to recommend an entity.
- AI representation drift : variation in the portrait generated by systems.
- LLM perception drift audit : protocol comparing canon, outputs, and perception trajectories.
The complete path is organized in the LLM perception drift and AI perception drift hub.
In this section
Canonical definition of an AI perception baseline, the documented initial state that makes it possible to measure perception drift over time.
Canonical definition of AI perception drift, meaning the change in representation produced by generative systems around an entity, brand, offer, or doctrine.
Canonical definition of AI perception stability, the capacity of an entity to be reconstructed faithfully, consistently, and without contradiction by several generative systems.
Canonical definition of AI representation drift, the variation in the portrait reconstructed by generative systems before a perception effect is even observed.
Canonical definition of category drift, where AI systems place an entity in the wrong market, the wrong neighborhood, or an overly generic class.
Canonical definition of cross-model drift, where several AI systems reconstruct the same entity, offer, person, brand, or doctrine differently.
Canonical definition of LLM perception drift, the change in how large language models reconstruct, describe, classify, or recommend an entity.
Canonical definition of an LLM perception drift audit, an observation protocol comparing canon, generative outputs, models, and representation trajectories.
Canonical definition of recommendability drift, where a system’s propensity to recommend an entity changes without full visibility loss.
AI citation readiness defines whether a source is accessible, retrievable, extractable, citable and governable in AI-mediated answers.
An AI-ready content block is a visible, self-contained section designed to be retrieved, extracted and cited without losing scope or source hierarchy.
AI-ready structure describes page organization that makes important passages easier to retrieve, extract and evaluate without losing scope.
An answer-ready passage is a self-contained section designed to support a specific answer without losing scope, date, source role or exclusion.
Citation accessibility is the condition in which a source and its useful passages can be reached, rendered and reused by systems expected to cite them.
Citation fidelity evaluates whether a displayed citation actually supports and constrains the claim made by an AI-mediated answer.
Citation-output gap names the mismatch between what a cited source supports and what an AI answer actually says.
Citation quality is the diagnostic value of a citation based on support strength, source role, freshness, legitimacy and fidelity to the claim.
A citation readiness audit evaluates whether a source can be accessed, retrieved, extracted, cited and governed before citation tracking begins.
Citation role classifies what a displayed citation actually does inside an AI-generated answer.
Citation stability is the persistence of citation patterns across prompts, systems, languages, locations and time.
A discovery surface helps machines or readers find relevant routes, but does not itself govern interpretation or prove a claim.
Extractability is the capacity of a passage, claim or page section to be segmented and reused without losing its meaning.
A fan-out query is a sub-query generated or implied by an AI answer system to ground a broader user question.
An interpretive 404 is a 404 response produced by a non-existent but plausible URL, revealing a documentary expectation rather than a simple broken link.
Known-source risk is the risk that an AI system relies on a source it believes it knows, including stale or reconstructed URLs.
A latent documentary surface is an unpublished page or content surface suggested by the conceptual structure of a corpus.
Machine-first routing defines how pages, definitions, services and artifacts guide automated readers toward the right source for the right claim.
A phantom citation is a displayed or implied citation to a source that does not exist, no longer exists, or does not support the claim.
A phantom URL is an unpublished, non-existent URL requested in a form that remains coherent with a site’s documentary architecture.
Preview control describes how snippet and extraction directives shape what search and answer systems may display or reuse from a page.
Retrieval rank describes the relative position or priority of a source during answer construction, distinct from classic search ranking.
Retrieval without citation describes cases where a source appears to influence an AI answer without being displayed as a citation.
A self-contained passage preserves enough entity, claim, scope and limit information to be extracted without distortion.
Source legitimacy defines whether a source is authorized to govern a claim, beyond being visible, popular, cited or retrieved.
Source substitution occurs when an AI answer replaces the canonical governing source with a secondary or more convenient source.
The Accessibility Tree exposes roles, names, states, and relationships in an interface, making it an action map for agents.
Agentic navigability measures an AI agent’s ability to understand, traverse, and manipulate a web interface without operational ambiguity.
The agentic web is the regime in which a website becomes an interpretable, navigable, and actionable environment for AI agents.
An interpretable interface clearly exposes its visual, structural, and programmatic intentions so that an agent or human can understand available actions.
Accountability surface defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of agentic memory: persisted, reusable state that can guide later AI responses, tool calls, delegations, or actions.
Agentic response conditions defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Agentic risk defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical bridge definition of AI answer audit: the structured review of generated answers against canon, source hierarchy, proof, and response legitimacy.
Canonical definition of AI brand representation: the way AI systems reconstruct, summarize, compare, or recommend a brand from available sources and signals.
AI brand representation audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI citation analysis defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI citation tracking defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI citation tracking audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI search monitoring defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI search optimization defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI search optimization audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI source mapping defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI visibility audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of answer legitimacy: the conditions that determine whether an AI system should answer, qualify, refuse, escalate or expose uncertainty.
Canonical definition of arbitration: the mechanism by which a system chooses, exposes or refuses between competing interpretations, sources or response paths.
Authority boundary defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Brand visibility in ChatGPT defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Brand visibility in ChatGPT audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canon maintenance defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical fragility defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical refresh cycle defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical source defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical surface defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Challenge path defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Citability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Citability audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Commitment boundary defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Comparative audits defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Contestability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Controlled forgetting defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Correction backlog defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Correction budget defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Correction resorption defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Default inference defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of defensible inference: bounded inference that can be reconstructed and challenged.
Deprecation discipline defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Drift detection defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Enforceability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Execution boundary defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of free inference: model inference that goes beyond the retrieved or authorized corpus without an explicit governance basis.
Generative engine optimization defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Generative engine optimization audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
GEO metrics defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Independent reporting defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Indeterminacy defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of inference boundary: the declared perimeter inside which a system may infer without crossing into unauthorized completion.
Interpretation trace defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive auditability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive debt defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive error space defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive fidelity defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive inertia designates an AI system's resistance to modifying an already stabilized interpretation, even after canon correction or clarification.
Canonical definition of interpretive legitimacy: the conditions under which an AI interpretation may be produced, assumed, cited or relied upon.
Interpretive maintenance defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive remanence designates the persistence of an old interpretation in AI outputs, even after the canon has been corrected, clarified, or updated.
Interpretive risk defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive risk assessment defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive sustainability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Legitimate non-response defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Liability reduction defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
LLM visibility defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
LLM visibility audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Maintenance burden defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Mandatory silence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Memory governance: doctrinal extension applied to stateful systems (agents, advanced RAG, persisted memories) to prevent inference fossilization into facts.
Canonical definition of memory object: a typed unit of persisted state with source, authority, temporal validity, scope, and invalidation conditions.
Obsolescence control defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Opposability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Persistent assumptions defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Pre-launch semantic analysis defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Procedural validity defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of proof of fidelity: the minimum evidence required to show that an AI output remains faithful to the canon rather than merely plausible.
Recommendability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Recommendability audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical market-bridge definition of representation gap audit: diagnosis of the distance between canonical self-description and AI-mediated reconstruction.
Canonical definition of resorption: the gradual neutralization, absorption, or deactivation of an obsolete, distorted, or residual interpretation.
Response conditions defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic debt defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Source hierarchy defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Stale-state handling defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
State drift defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Surviving authority defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Version power defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Agentic defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI governance JSON defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
AI manifest defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of chunk authority: the limited authority carried by a retrieved passage or fragment within its source, perimeter, and context.
Corpus admissibility defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Cross-layer transactional coherence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Cross-system coherence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Delegated action defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Delegated meaning defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Documentary architecture defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of documentary chain: the sequence linking canonical sources, retrieval, provenance, evidence, versioning, and answer construction.
Entity collision defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Entity disambiguation defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Entity graph defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Framing stability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Global exclusions defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive drift defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive SEO vs Entity SEO vs GEO vs AEO defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Machine-first artifacts defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Machine-first canon defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Machine readability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Multi-agent chains defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Non-agentic systems designate AI systems that produce an output without planning and executing a tool-driven action sequence oriented toward an objective.
Non-inference regime defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
RAG governance defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Reading conditions defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of response web: the web environment in which search, LLMs, agents, summaries, citations, and recommendations transform pages into answers.
Retrieval control defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of retrieval provenance: the traceable record of which sources, chunks, versions, and retrieval conditions influenced an AI answer.
Semantic accountability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic architecture defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic contamination defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic integrity defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic neighborhood defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Source admission defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Tool-mediated authority defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Transactional coherence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Authority conflict defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Authority ordering defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canon-output gap defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Durable interpretive presence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Evidence layer defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Governed negation designates a canonical property where an entity, corpus, or system explicitly declares what is not true, not covered, or must not be inferred.
Inference prohibition defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretation integrity audit defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
The legitimate locus from which the meaning of a statement, entity, doctrine, state, policy, or public claim may be defined, bounded, corrected, or suspended.
Interpretive evidence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive observability defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive perimeter defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of manufactured coherence: the smoothing process by which an AI system hides gaps, conflicts or missing authority behind a fluent answer.
Q-Ledger defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Q-Metrics defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Reconstructable evidence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Silent delegation of authority defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Surface coherence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical definition of unauthorized synthesis: an AI answer that combines real fragments into a conclusion no governing authority authorized.
Defined authority defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Inferred authority defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Statement-level authority defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Stabilized state of the web defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Citation persistence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Distributed interpretive authority governance defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Structural visibility defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Early machine visibility defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Exogenous governance (short definition) defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
External Authority Control (EAC). Canonical definition within interpretive governance, semantic architecture, and AI systems.
AI disambiguation defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical silence defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Compliance drift defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Endogenous governance defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
The external coherence graph designates the mapping of public signals that frame how an entity is interpreted by AI systems in the open web.
Interpretability perimeter defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive capture defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive collision defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive governance defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive hallucination defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive invisibilization defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive SEO defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Interpretive smoothing defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
The interpretive trail designates the transitory state where a canonical correction begins producing effects, but incompletely, irregularly, or contextually.
Neighborhood contamination defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Post-semantics (thinking & reasoning) vs interpretive… defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic calibration defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Semantic compression defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
SSA-E + A2 + Dual Web defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Authority Governance (Layer 3) defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.