Invisible characters hide machine text in the human range: normalize, then calibrate, and recall rises 5 points at 0.1% false positives
Web platforms screen posts for large-language-model text at a very low false-positive rate, and invisible edits such as homoglyphs and zero-width spaces push machine text into the human score range. NormDetect normalizes each document with Unicode rules, then anchors a small per-section classifier head to the normalized Binoculars zero-shot score, with the blend weight chosen on training folds under a human false-positive cap. On eight RAID sections and eleven attacks with the Falcon-7B scoring pair, it recalls 55.5% at a 0.1% calibrated false-positive rate, 5.0 points above normalized Binoculars, while every section's human false-positive bound stays at most 1%.