HyGRAIL is a framework for discovering scientific hypotheses in incomplete knowledge graphs by combining a graph neural network (GNN) triage with large language model (LLM) review. The GNN scores candidate hypotheses and routes only ambiguous cases to the LLM, which receives structured evidence from the graph converted into natural language. Experiments on MatKG show HyGRAIL achieves the highest F1 score, improves over baselines, and cuts LLM calls by over 54%.
By Yihang Sun, Zhihan Zhu, Zhiyuan Jiang, Jingyi Ge, Zixuan Li, Jiaxuan You
Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed links in such graphs can therefore represent plau...
arXiv:2607. 21859v2 Announce Type: replace Abstract: Constructing causal directed acyclic graphs (DAGs) is a core step in biomedical causal analysis, yet it remains a largely manual process.
By Yi-han Sheu, Michael R. Steigman, Yu Zhou, Bo Wang, Fan-Yu Yen, Jordan W. Smoller
arXiv:2607. 20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources.
By Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen
Accumulated scientific knowledge advances inquiry when prior findings help researchers choose new questions, design investigations, and interpret results. Realizing this value at scale requires access...
The paper introduces GI-TraceBench, a benchmark of 10,282 gastrointestinal oncology questions that combines provenance question answering, knowledge discovery, and hypothesis evaluation using shared Paper, Claim, and Evidence Span records. It proposes FindLink, a two‑stage framework that first grounds premises with explicit study context and support status, then structures these premises into support plans to generate evidence‑conditioned answers. On a 1,545‑question test set, FindLink raises the balanced family macro score from 67.95 to 87.25 compared to direct supervised fine‑tuning, demonstrating the importance of both premise identification and connection assessment in scientific reasoning.
By Jingxuan Wei, Song Tan, Xiaohan Liu, Chao Sang, Changwei Wang, Linzhuang Sun, Bihui Yu, Fuqiang Wang, Junnan Zhu