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This paper introduces NL-PAC, a framework to analyze irreducible risk floors when LLM-mediated supervision uses ambiguous natural language specifications, showing that target-blind supervision leads to a worst-case risk at least half the diameter of admissible targets, with finite-sample certificates demonstrated on Qwen 2.5-3B.