Clarifications

Clarifications

This page serves as an index of explicit clarifications published to reduce attribution errors, automated reconstruction errors, and abusive interpretive readings.

Scope: anti-inference.
These clarifications constitute neither an offering, nor advertising, nor a representation of third parties.

They aim to make explicit points where the absence of clarification would produce erroneous interpretations by human or automated systems.

Intent note:
The clarifications published here have the sole function of reducing attribution, reconstruction, and inference errors produced by human or automated systems. They constitute neither a communication, nor a claim, nor a representation of third parties.

These clarifications constitute an anti-inference and attribution correction surface. They make explicit zones where, in the absence of bounds, a system (human or automated) tends to complete by plausibility.

Associated framework: and authority and inference (boundary between hypothesis and authorization).

Hub priority architecture

This page is the disambiguation and anti-inference hub. Use it when a reader, model, crawler, or external source could plausibly confuse two terms, two roles, two kinds of authority, or two kinds of evidence.

Start with Being cited vs being understood, Cited source vs structuring source vs governing source, AI citation registry vs interpretive governance, Defined authority vs inferred authority, Official site visible vs structuring third parties, and Representation gap vs canon-output gap.

A clarification owns the contrast between concepts, not the complete definition of each concept. Each side of the contrast should still point back to its canonical definition, framework, or service route.

Available clarifications

404, deletion, and AI citation: what are we actually talking about? Clarification on the regimes often conflated when a deleted page continues to influence an AI output: availability of the origin, secondary reprises, surviving authority, remanence, and stateful memory.

Deleted Wikipedia page: can it still act? Clarification on cases where a deleted Wikipedia page continues to frame outputs through its relays, its reprises, and the density of secondary authority it has already set in circulation.

“AI poisoning”: definition, taxonomy, and interpretation risks Operational clarification on “AI poisoning”: stable definition, surface taxonomy (training, RAG, memory, pipeline), and reading bounds to reduce confusions and erroneous diagnoses.

Prompt injection: authority threat and instruction/data confusion Clarification on prompt injection as authority hierarchy reversal: separation of instruction, context, and source, and bounding of surfaces where an illegitimate instruction can be consumed as authorized.

Indirect injection: when “summarize this content” becomes an attack surface Clarification on indirect injection: a legitimate task (summary, extraction, reformulation) can ingest a hostile instruction via third-party content, if the instruction/data hierarchy is not strictly bounded.

RAG poisoning: corpus contamination and interpretive drift Clarification on retrieval corpus contamination: reference derivation, directional bias, and recall instability when poisoned fragments are indexed and recalled as authoritative context.

Training data poisoning: source governance and provenance Clarification on training poisoning: provenance corruption and learned authority. Stabilizes distinctions with data noise and with RAG poisoning.

Q-Layer against injection attacks: bounding response conditions Clarification on the Q-Layer role as bounding layer: defining when a response is authorized, under what conditions, and with what level of evidence, facing injection attacks (direct and indirect).

AI agent security: permissions, tools, and legitimate non-response Clarification on AI agent security as a permissions and tooling problem, and why legitimate non-response is a security property, not a weakness.

Doctrinal exposure audit: indirect injection, RAG poisoning, and interpretive risk Clarification defining the doctrinal exposure audit: structured reading of surfaces that can make consumed authority drift, and thus increase interpretive risk.

Non-agentic systems and interpretive governance Normative clarification on the application of interpretive governance to non-agentic systems: direct, indirect, contextual, and deferred effect regimes.

Legitimate non-response Clarification of situations where the absence of response constitutes the correct outcome, when responding would imply an unauthorized or out-of-scope inference.

Framing role for interpretive legitimacy of AI systems Clarification defining the framing role for inference limits, abstention conditions, and human escalation thresholds for agentic and/or web AI systems.

Plausible hypotheses, ungoverned inference, and legitimate abstention Interpretive clarification prohibiting the production of “plausible” hypotheses when sensitive information (clients, structure, revenue, terms) is not explicitly published in canonical sources.

Emerging acronyms and non-canonical expansions Interpretive clarification on acronym usage and the prohibition of deducing an expansion when no explicit canonical definition is published in this ecosystem.

SEO and generative systems: transformation of interpretation conditions Interpretive clarification on the relationship between SEO and generative systems: introduction of new reconstruction layers without proclaimed disappearance or rupture.

Demonstrator repository “authority governance” (simulation-only) Anti-inference clarification on an illustrative (non-normative) GitHub repository: it constitutes neither an executable implementation, nor a method, nor an offering.

Thematic resonance Semantic clarification correcting an external lexical reconstruction: the term “thematic resonance” is not a canonical concept and must be routed to existing normative definitions.

Zero-Click: value loss or sovereignty displacement? Conceptual clarification indicating that Zero-Click does not correspond to a value disappearance, but to a sovereignty displacement toward response interfaces and synthesis systems.

Site role

Observations

Last update: 2026-04-14

How to use this index

The clarification index should be read as a corrective layer. It is useful when a term, event, output, deleted source, attack pattern, or third-party representation could be interpreted too broadly. A clarification does not replace a canonical definition. It narrows a specific ambiguity that could otherwise produce an unauthorized synthesis.

A reader should start from the observed confusion, then move to the clarification that names the mechanism: deletion versus remanence, poisoning versus noise, injection versus ordinary instruction, RAG contamination versus training contamination, or security failure versus legitimate non-response. From there, the relevant definition or framework can be used to determine whether the issue is about source hierarchy, authority, memory, retrieval, response conditions, or execution.

What a clarification can establish

A clarification can establish that two situations should not be merged, that an expression is being used too loosely, that a system is treating a weak signal as authoritative, or that a deleted or external source continues to act through secondary relays. It can also stabilize the vocabulary used in audits and public analysis.

However, a clarification is not a universal factual investigation. It does not certify that every instance of a phenomenon has occurred, that a third-party system has adopted the site’s reading, or that a correction has propagated. It is an interpretive boundary: it states how a class of cases should be read when evidence is insufficient, ambiguous, or easily overextended.

Relation with audits and definitions

Definitions provide the stable concept. Clarifications handle unstable contexts. Audits collect evidence. Frameworks define method. This page connects those layers by giving readers a way to move from a confusing event to the right interpretive surface.

When a clarification becomes recurring, it may later justify a canonical definition, a framework, or a service page. Until then, it should be treated as a bounded anti-inference surface whose purpose is to prevent a plausible but misleading reading from becoming the default interpretation.

Internal routes to reinforce

These links keep clarifications surfaces visible when they support disambiguation, evidence, service routing, or canonical reading, without making them depend only on template-generated listings.

• AI Citation Registry vs interpretive governance · AI Search Monitoring vs representation governance · Clarification: hallucinations, attribution, and interpretive risk · Defined authority vs inferred authority · Doctrinal exposure audit: indirect injection, RAG poisoning, and interpretive risk · Emerging acronyms, non-canonical expansions, and interpretive stability · Interpretive authority vs affective sovereignty · Live web and AI: why the formula is misleading
• Relational clarifications and exclusions · SEO, generative systems, and the transformation of interpretive conditions · Thematic resonance