MedRAGChecker is a claim-level verification framework designed for biomedical retrieval‑augmented generation (RAG). It decomposes generated answers into atomic claims and assesses each claim’s support by combining evidence‑grounded natural language inference with biomedical knowledge‑graph consistency signals. The aggregated claim decisions provide diagnostics that distinguish retrieval and generation failures, such as faithfulness, under‑evidence, contradiction, and safety‑critical errors, and the system is distilled into compact models for scalable evaluation.
By Yuelyu Ji, Min Gu Kwak, Hang Zhang, Xizhi Wu, Chenyu Li, Yanshan Wang
The paper introduces BioCheck Agent, an LLM-based system that generates structured biomedical fact‑checking reports using agentic search and a reinforcement‑learning framework called EG‑GRPO. Unlike prior methods that output only supported or refuted labels, BioCheck Agent synthesizes conclusions with retrieved evidence from PubMed, employing advanced Boolean search operators. Experiments show that, compared to the base Qwen3.5‑4B model, BioCheck Agent improves label prediction accuracy on SciFact by 9.95 %, raises evidence quality by 3.7 %, and reduces hallucinations by 19.63 %.
By Jiongxiao Wang, Dingli Ma, Chaoqun Ni
The paper introduces CLEAR, an agentic framework designed to improve the reliability of large language models (LLMs) in medical contexts by adjudicating evidence from multiple sources. CLEAR generates candidate answers from three distinct pathways—parametric knowledge, locally curated corpora, and dynamically retrieved evidence—and then uses an aggregation verifier to evaluate agreement and conflict among these sources. An adjudication module decides whether to preserve or revise conclusions, employing override-guard and challenge-audit mechanisms, and initiates targeted follow-up searches when conflicts remain unresolved.
By Shuai Wang, Yize Zhao, Qingyu Chen
The paper investigates why retrieval‑based open‑ended evaluation fails in medical fact verification. By creating two detailed taxonomies—one for retrieval‑stage errors across five quality dimensions and another for verifier‑reasoning errors across six steps—the authors automatically label evidence quality and reasoning errors using an LLM‑as‑Judge pipeline. Their large‑scale stress tests across multiple retrieval methods and verifier models show that increasing model size, reasoning effort, source breadth, or medical fine‑tuning does not eliminate these failure modes, indicating fundamental limits of the retrieve‑then‑verify paradigm in open‑ended medical contexts.
By Heyuan Huang, Jirui Dai, Alexandra DeLucia, Sonal Joshi, Mahsa Yarmohammadi, Jie Gao, Bernal Jim\'enez Guti\'errez, Mark Dredze
The paper introduces BAR, a Budget‑Aware LLM Reasoning framework that enhances post‑discharge risk prediction by integrating external medical knowledge graphs (KGs). BAR refines KGs into disease‑specific evidence graphs with support scores and provenance, then uses an LLM to plan, navigate, and verify evidence within a patient‑specific budget. Experiments on MIMIC‑III and MIMIC‑IV across eight diseases show BAR improves AUPRC by 3.4 points, raises citation precision from 59.8% to 77.9%, and uses only 62‑65% of the allotted budget.
By Chen Chen, Dongjie Wang, Mei Liu, Zijun Yao
The paper "Medical Causal Hypothesis Verification with Large Language Models" reports a small-scale study evaluating eight LLMs on 17 medical causal hypotheses. The authors introduce an evaluation framework and annotate 1,067 evidence points across six criteria, using nine metrics to assess performance. Results show that while LLMs have strong recall, they frequently fail to provide valid scientific articles, evidence, or reject unsupported hypotheses, revealing a critical limitation for their use in healthcare.
By Safiyyah Ahmed, Abrar Ansari, Md Aminul Islam, Elena Zheleva