Grounded Revision vs. Prior Injection: Probing Retrieval-Augmented Patent Claim Amendment

Title:Grounded Revision vs. Prior Injection: Probing Retrieval-Augmented Patent Claim Amendment

Abstract: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.

Accepted to Findings of AACL-IJCNLP 2026. 9 pages, 2 figures. Code and data: this https URL

Computation and Language (cs.CL)

arXiv:2609.36550 [cs.CL]

(or arXiv:2609.36550v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.36550 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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