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Canon-output gap as a measure of LLM perception drift

How the gap between canonical source and generated output makes it possible to qualify LLM perception drift.

CollectionArticle
TypeArticle
Categoryinterpretation ia
Published2026-05-15
Updated2026-08-08
Reading time2 min

Canon-output gap as a measure of LLM perception drift

LLM perception drift becomes measurable when a dated output can be compared with a dated reference state. The canon-output gap asks which properties, boundaries and authority roles were preserved or transformed, not whether the answer merely “looks good.”

Two bounded objects

The canon contains claims the organization or another competent source may legitimately establish. The output contains generated text, context, citations and uncertainty. Neither should be idealized.

Canon may be incomplete or promotional. Output may include legitimate external evidence. The gap is not simply “different from the official site.”

Comparison dimensions

Dimension Question Example gap
Identity Is the correct entity named? Namesake confusion
Category Is the primary role preserved? Specialist reduced to generalist
Scope Are offer and exclusions respected? Out-of-market service added
Time Is current state distinguished? Old product shown as active
Relations Are affiliations and competitors correct? Subsidiary turned into partner
Authority Does a competent source govern the claim? Old directory against current policy
Non-implication Does output exceed evidence? Expertise turned into leadership
Reputation Are evaluations attributed? Opinion presented as consensus

Coding method

Classify each invariant as preserved, partial, displaced, contradictory, legitimate external or not observable. This prevents treating every omission as error and every criticism as drift.

Example

The canon describes a firm as a B2B specialist in governing AI answers. An output calls it an SEO agency using AI tools. Name and some skills are preserved, but category, hierarchy and audience are displaced. Sentiment is neutral and no spectacular fact is invented.

The useful measure is not sentiment. It is invariant preservation, gap severity and repetition across intents.

Aggregates and critical incidents

A global index may help tracking, but must not hide rare severe cases. Report dimension aggregates, prompt-family distribution, critical incidents, cross-model and cross-language divergence, structuring sources and confidence.

Temporal comparison

Drift requires comparable windows. Measure what appears, disappears, repeats or changes in severity. Model, browsing or prompt changes may invalidate comparison; protocol versions must remain visible.

Limits

The canon-output gap does not prove market acceptance, directly measure public image or brand equity, or establish the cause of output. It provides an evidence object: what was established, what was produced and how they diverged.