arXiv Computation and Language September 3, 2026 By Yihang Sun, Zhihan Zhu, Zhiyuan Jiang, Jingyi Ge, Zixuan Li, Jiaxuan You

HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs

Read the original on arXiv Computation and Language →

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%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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