Medical LLMs are commonly evaluated on factual question answering, summarization, and explicitly stated clinical information, but less on pragmatic understanding: recovering intended meaning from indirectness, hedging, urgency cues, politeness, and implicature. We present PCP, a 3,533-item benchmark for pragmatic intention recognition in clinical communication, with a controlled synthetic Core split, a close-distractor Hard split, and a small externally grounded Real-Clean split derived from public doctor–patient dialogue datasets. PCP combines answer-choice artifact audits with blind human validation of all Hard and Real-Clean items plus a 300-item Core sample, and targeted expert review of 280 items. Across eleven systems (six API-accessed, five open-weight), our main finding is a recognition-vs-naming dissociation: ten of the eleven select the intended clinical action at 95.5–98.4% aggregate accuracy but name the pragmatic phenomenon licensing it at only 56.6–64.5%, a 32–40 pp gap on the same items. Recognition is also split-dependent: API-accessed models are near ceiling on PCP-Core (98.7–99.8%) but drop on PCP-Hard-469 (83.4–90.6%) and PCP-Real-Clean-64 (79.7–90.6%), so broad controlled probes can mask difficulty exposed by close distractors and externally grounded cases. Because gold actions are often the most medically defensible choice, accuracy is a confound-limited estimate; PCP is an evaluation resource for controlled clinical-pragmatic inference, not for deployment readiness.

We provide a confound-controlled methodology and a released "wrong-question" diagnostic for internals-based, reference-free hallucination detection, which treats context-faithfulness (is an answer entailed by the context?) as a linear residual-stream direction. In the standard extractive setup, faithfulness is almost perfectly correlated with lexical overlap (r=0.99), so an apparent "grounding probe" may be a string-matching detector. Our matched Support×Overlap construction partially decouples the two (halving the confound to r=0.50), and the diagnostic fools an off-the-shelf NLI model. Across four instruction-tuned 7–9B families: (i) within the overlap-controlled stratum a support contrast is linearly decodable, but a no-context ablation shows it reflects question–answer compatibility rather than context use, and is inseparable from factoid-correctness by construction; (ii) it aligns across families up to a linear map, yet answer-length, overlap, and factuality directions align equally well, a generic matched-stimulus property, not grounding-specific; (iii) on the three families we could extract, the synthetic direction does not transfer to real hallucinations, scoring below NLI, confidence, and lexical overlap on RAGTruth, where only an in-domain probe trained on human labels recovers a usable signal.