EvidenceNet is a disease‑specific dataset that transforms full‑text biomedical literature into structured evidence records and graph representations, preserving study design, provenance, and quantitative support. Using an LLM‑assisted pipeline, it extracts experimentally grounded findings, normalizes entities, scores evidence quality, and links related records via typed semantic relations. The released subsets—EvidenceNet‑HCC and EvidenceNet‑CRC—contain thousands of evidence records and richly connected graphs, with high extraction and relation‑type accuracy, enabling retrieval‑augmented question answering and graph‑based tasks such as link prediction and target prioritization.
By Chang Zong, Jinyu Chen, Sicheng Lv, Si-tu Xue, Huilin Zheng, Jian Wan, Lei Zhang
arXiv:2606. 28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.
By Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang
arXiv:2602. 17911v3 Announce Type: replace-cross Abstract: Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every decision depends on patient-specific factors such as comorbidities and contraindications.
By Jash Rajesh Parekh, Wonbin Kweon, Joey Chan, Rezarta Islamaj, Robert Leaman, Pengcheng Jiang, Chih-Hsuan Wei, Zhizheng Wang, Zhiyong Lu, Jiawei Han
arXiv:2609.14528v1 Announce Type: cross
Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
By Eduin E. Hernandez, Luis F. Garcia, Nurassyl Askar, Sergio A. Diaz, Stefano Rini
SearchAtlas is a framework that transforms raw search trajectories of large language model (LLM) agents into structured evidential query graphs, where edges capture how evidence is propagated from queries to the final answer. The automated parsing pipeline achieves a mean edge F1 of 86.0% against human-annotated graphs and remains consistent across repeated runs. Using SearchAtlas, the authors analyze five search agents on three benchmarks, uncovering systematic differences in search scale and evidence aggregation, and revealing process failures such as fragmented answer support, unmet question constraints, and unverified parametric knowledge that correlate strongly with incorrect answers.
By Jiacheng Sang, Mengyuan Li, Sanxing Chen, Yukun Huang, Yu Feng, Bhuwan Dhingra
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:2607. 06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents.
By Taeyun Roh, Eunha Lee, Wonjune Jang, Sohyun Chung, Junha Jung, Jaewoo Kang
The paper introduces Constrained Entity Selection under Partial Knowledge (CES-PK), a framework for improving large language model (LLM) based knowledge graph question answering (KGQA) by filtering candidate answers with lightweight symbolic constraints instead of full semantic parsing. CES-PK uses a three-valued constraint semantics—satisfied, violated, unknown—to handle incomplete knowledge graphs and avoid incorrect rejections under open‑world assumptions. Experiments on the Hetionet biomedical knowledge graph show that applying type, relation, and exclusion constraints increases precision while preserving recall, and that satisfied constraints can be used to rank remaining candidates.
By Emanuel Kitzelmann
arXiv:2607. 19678v1 Announce Type: cross Abstract: AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer.
By Guneet Singh Kohli, Yuxiang Zhou, Michael Sejr Schlichtkrull, Gregory E Dean, Maria Liakata
arXiv:2607. 22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships exhaustively, producing graphs whose size and construction cost scale with corpus length rather than with the reasoning a query requires.
By Marc Saouda (Boston Consulting Group), Rajprakash Bale (Boston Consulting Group), Eren Aldis (Boston Consulting Group), Cloves Almeida (Boston Consulting Group)
arXiv:2607. 10795v1 Announce Type: new Abstract: In open-domain multi-hop question answering (QA), LLM-based search agents offer a promising approach to knowledge-intensive QA by combining retrieval with reasoning.
By Xinkang Li, Rong Jiang, Xin Song, Ye Wang, Yue Han, Changjian Li
arXiv:2609.06779v1 Announce Type: cross
Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cos...
By Zijie Liu, Hongxuan Li, Zhen Tan, Jinhao Duan, Baixiang Huang, Zunpeng Liu, Kai Shu, Tianlong Chen