Canon-output gap as a measure of LLM perception drift

Canon-output gap as a measure of LLM perception drift

To measure perception drift, mention tracking is not enough. An output must be compared with a reference source.

This article belongs to the LLM perception drift / AI perception drift cluster. It connects emerging market vocabulary to a deeper issue: AI systems do not only cite entities, they reconstruct them.

The canon makes the gap observable

The canon sets the admissible state of the entity. The output shows what the system reconstructs. The gap between the two becomes the minimal measurement unit.

Not every gap is drift

A one-off gap may be normal variation. Drift appears when the gap repeats, widens, stabilizes, or propagates across several systems.

Measurement must remain interpretive

The objective is not to produce a magic score. The objective is to understand which part of the representation changed and which signal must be corrected.

Implication for interpretive governance

Perception drift should be read with AI perception drift, canon-output gap, proof of fidelity, and interpretive risk.

The task is not to make the brand noisier. The task is to make its representation harder to reconstruct incorrectly.

Conclusion

The move from classic SEO to generative AI requires a shift: we no longer govern only pages and rankings, but reconstruction conditions. This is exactly where perception stability becomes a strategic asset.