Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question. Measuring that gap takes care: a model answering A to most items scores above chance wherever the key sits at A, and reads as recognition when it is not. We measure it on UA-JudgeExam: 11,990 four-option items with official keys, published by Ukraine's Higher Qualification Commission of Judges. Shown the options and no question, Claude Haiku 4.5 scores 0.383 against chance, and the leak is concentrated: 11.8% of items are answered blind on all eight option orders, against 0.2 items expected by chance. It is not quotation: search over 280,059 editions of Ukrainian legislation recovers 0.128. Gating those out retains 8,128 items, on which the gating model itself now scores 0.204, while GPT-5.6, which took no part in the selection, still answers 0.515 of them with the question hidden. Scoring eighteen held-out models from eight vendors on the whole set and subtracting each one's position habit, four keep a sizeable excess: GPT-5.6 (+0.265), Gemini 3.1 Pro (+0.240), GPT-5.4 (+0.105), Sonnet 4.6 (+0.081); the rest stay within 0.031, and probes find no recall of the public bank behind it. Without the correction Llama 3.1 8B (0.292 blind, A on 92% of items) would read as a leaker. The gate does select something real: on the items it rejected, eleven of twelve models score 0.518-0.789, every interval clear of what the same model scores on the items it kept. But that signal is one model's, and filtering on it does not transfer upward. Rewriting distractors instead overshoots to 0.168, below chance and as exploitable. The same probe on LEXam returns chance: every option there points into the stem, none longer than 33 characters. Item format decides whether the problem can arise; capability decides how much is extracted. We release the corpus, predictions and harness.

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