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
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.
By Qiao Jin, Yin Fang, Lauren He, Yifan Yang, Guangzhi Xiong, Zhizheng Wang, Nicholas Wan, Joey Chan, Donald C. Comeau, Robert Leaman, Charalampos S. Floudas, Aidong Zhang, Michael F. Chiang, Yifan Peng, Zhiyong Lu
arXiv:2608.29582v1 Announce Type: cross
Abstract: Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigati...
By Yi Yu, Bo Wang, Chong Feng, Ge Shi, Xia Liu, Ziyi Yang, Xuewen Shi
The article presents a new semantic model for representing scientific evidence, specifically tailored to genetics, that extends existing standards by adding fine‑grained, domain‑specific structure. It aligns with FHIR Evidence and SEPIO, incorporates a compact vocabulary validated by SHACL, and was tested in a human‑AI annotation pilot on six genetics papers, producing 28 evidence items and 95 source‑anchored assertions. The authors argue that this model advances trustworthy, AI‑ready infrastructure for variant interpretation by providing a reference data model and validation schema for genetic evidence.
By Michael Bouzinier, Dmitry Etin
The article proposes a framework called quantitative evidence mining to transform biomedical findings into structured, context-rich evidence units. It outlines core elements such as claim, measured entity, value, comparator, population, conditions, temporal context, uncertainty, provenance, validation, and expert review. The authors present an eight-stage reference architecture and emphasize that plausibility should remain multidimensional rather than collapsed into a single truth label, linking extraction to evidence synthesis for applications like clinical trials, biomarker research, and knowledge-graph construction.
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs
arXiv:2608.28607v1 Announce Type: new
Abstract: Pharmaceutical sponsors developing a drug for both the United States and the European Union must reconcile guidance issued independently by the FDA and...
By Chuchu Wu, Zhiyin Zhou, Jingzhuo Hu, Liang You