memorization_check.py answers HOW OFTEN an arm collides with the training text.
It does not answer WHAT is being reproduced, and those have different
consequences: a low rate hiding one 30-word run of distinctive imagery is worse
than a high rate of stock dialogue in the commonest words in English.
The lv-hemingway gate shipped at 7x the author's own collision rate precisely
because that reading was done -- every matched run was stock dialogue, max 9
words, no proper noun. But it was done BY HAND and left no instrument, so the
next gate had to repeat it by hand or skip it. This is it.
Prints every maximal matched run sorted by length, with arm/id/seed, and flags
any token capitalised mid-run as a possible proper noun. The flag deliberately
over-reports (sentence-initial I, He, What all trip it) because it is a reading
aid and over-reporting is the safe direction. --train-only matches
memorization_check.py so a collision with held-out text is not reported as
memorisation of training text. A corpus slice is matched against the corpus
every run, and the script REFUSES if that positive control fails -- a matcher
that only ever sees candidate text cannot tell "no matches" from "blind".
Validated against the lv-hemingway record's documented reading, which it
reproduces to the word: longest run 9 words, "swift tristan" flagged as the one
name-shaped hit (it is the RENAMED invented name, not Hemingway's), and the
record's quoted examples -- "came over and sat down at the table", "i don t
think so the girl said" -- both present.
Required by GATE-PREREG.md AMENDMENT 1, which makes reading the matches part of
axis B rather than a follow-up, because McCarthy is in copyright with a living
estate and a match carrying distinctive imagery or a proper noun is
disqualifying in a way a rate number alone is not.
