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We study this task as a compact compositional inference problem: predictions for $H$ and for a mechanically negated hypothesis $\\neg H$ should agree under a deterministic negation map. Despite this simple structure, large language models (LLMs) can exhibit two practical failure modes: (i) negation inconsistency, where answers to $H$ and $\\neg H$ violate the required label mapping, and (ii) epistemic $\\text{Unknown}$, where the model abstains even when one side is entailed. We introduce CGD-PD, a lightweight, training-free test-time layer that combines neural 3-way classification, symbolic negation-consistency projection, and targeted binary entailment probes. 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