Retrieval-augmented generation is widely used in professional writing, yet whether retrieval grounds revision or merely injects templates is rarely tested where "correct" has a definable meaning.
Patent claim amendment supplies that signal: the examiner names the attacked limitation and cites prior art, providing per-case ground truth.
We release three artifacts:
- (i) a corpus of 7,385 USPTO prosecution cases with XML-aligned pre/post claims, rejection, and cited prior art;
- (ii) a seven-probe battery comparing random and structural-match retrieval as two policies under a fixed prompt scaffold;
- (iii) a deterministic five-channel metric (C1-C3 and C5 in main, C4 supplementary) requiring no LLM evaluation.
Across 9,600 pre-registered calls on four frontier LLMs (Claude Sonnet 4, Claude Haiku 4.5, GPT-5.4, GPT-4o-mini), no tested model exhibits detectable classical prior-injection behavior;
retrieval effects are small and direction-inconsistent between random and structural retrieval, and the null is unchanged under a dense (semantic) retriever, across retrieval depths k in {1,3,5,10}, and under a paraphrase-sensitive grounding metric.
Revision locality reveals a model-specific difference that the template channel misses.
The four-cell taxonomy, which we treat as exploratory, leaves the prior-injector cell unoccupied.