AI perception drift vs hallucination
A hallucination is an output that invents, distorts, or asserts without admissible source support. It may be spectacular, easy to refute, and sometimes unstable.
AI perception drift is different. It may contain true elements, but organize them in a way that produces a less faithful overall perception. The problem is not only a false sentence. The problem is the portrait that stabilizes.
Practical difference
A hallucination is often recognized through an invented fact: a non-existent date, a client never served, a fictional degree, or a fabricated quote.
Perception drift is recognized through framing: the wrong category, the wrong role, erased differentiation, an older version returning, a competitor used as an implicit model, or a recommendation formulated for the wrong reasons.
Hallucination may contradict the canon. Perception drift may bypass the canon by producing a plausible but impoverished version.
Why the distinction matters
Correcting a hallucination often requires strengthening or clarifying a specific proof. Correcting perception drift requires more structural intervention: canon, semantic architecture, disambiguation, internal linking, freshness signals, source hierarchy, and output observation.
An audit that only searches for hallucinations can therefore miss the main problem. It may call an answer “correct” because it contains no invented fact, while ignoring that the entity representation has shifted.
Diagnostic rule
Use hallucination when the error concerns a non-admissible fact. Use perception drift when the overall reconstruction frame changes, repeats, or stabilizes.
The test is not only factual. It is interpretive: does the output give a reader or agent a faithful representation of what the entity is, does, limits, and proves?