[2607.09349] Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

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arXiv:2607.09349 (cs)

[Submitted on 10 Jul 2026]

Title:Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

Authors:Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim<br>View a PDF of the paper titled Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation, by Cedric Caruzzo and 2 other authors

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Abstract:Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents. It does not check whether retrieved evidence is attributed to the correct entity.

A clinical RAG response can pass every automated check (zero hallucinations, near-perfect faithfulness, real citations) while presenting drug Y's clinical evidence as evidence about queried drug X. We term this deceptive grounding (DG): a failure invisible to faithfulness, hallucination, and citation checks because every claim is sourced from a real document, about the wrong entity.

Using a controlled factorial benchmark across 13 models, we find DG rates spanning 8-87% at peak adversarial conditions. Medical and biomedical fine-tuned models reach up to 86.7%; domain specialization amplifies the failure rather than mitigating it.

A controlled ablation identifies the mechanism: removing entity-specific clinical evidence from retrieved documents eliminates entity-attribution failure entirely, shifting all failures to confabulation. The two failure modes respond to the same trigger, taking different paths.

Production measurement across 740 drug-disease pairs finds 7.8% overall DG in a deployed RAG system, rising to 13.6% for recently approved drugs. Entity-attribution verification (checking that cited evidence applies to the queried entity) detects DG at 97.0% precision and 98.7% DG recall (IPW-adjusted human gold standard); no existing framework implements it.

Comments:<br>24 pages, 7 figures, 12 tables

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2607.09349 [cs.CL]

(or<br>arXiv:2607.09349v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.09349

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Cedric Caruzzo [view email]<br>[v1]<br>Fri, 10 Jul 2026 12:29:10 UTC (4,137 KB)

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