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cs.CL2026
Knowledge Graph-Augmented Ambient AI for Clinical Note Generation
Jakir Hossain, Yi-Fei Zhao, Hongjian Wang +9
Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to substantially reduce clinician…
cs.CL2026
What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA
Yuelyu Ji, Min Gu Kwak, Hang Zhang +3
Medical RAG needs evidence-grounded claims, so plugging a claim-level NLI checker into retrieval-augmented RL is intuitive. \textbf{We find that the checker's \emph{output distribu…
cs.CL2026
MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation
Yuelyu Ji, Min Gu Kwak, Hang Zhang +3
Biomedical retrieval-augmented generation (RAG) can ground LLM answers in medical literature, yet long-form outputs often contain isolated unsupported or contradictory claims with…