AdaPath 解决了生物医学知识图谱问答中的路径查找难题，在复杂查询中表现优异。
AdaPath 是一种路径查找框架，通过从 Path-Bank 中检索查询自适应的元路径来解决生物医学 QA 的挑战。该框架在 BioStrat-QA 基准测试中表现优异，即使多跳查询暴露的表面信息较少，也能保持有效的路径查找。研究团队发布了 BioStrat-QA 生物医学 KGQA 基准，该基准根据查询暴露的中间推理程度对多跳查询进行分层。AdaPath 在多个生物医学 KGQA 基准测试中持续优于基线方法。
AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two distinct challenges that general-domain methods are not designed for: (i) queries do not expose intermediate reasoning and can be answered through multiple valid pathways, and (ii) biomedical knowledge graphs are densely connected, so path-finding methods easily take wrong turns. To address these challenges, we propose AdaPath, a path-finding framework that retrieves query-adaptive meta-paths from Path-Bank, which captures both query semantics and biomedical knowledge graph structure. AdaPath provides the missing cues in biomedical queries while effectively pruning dense knowledge graph neighborhoods during multi-hop reasoning. We further release BioStrat-QA, a biomedical KGQA benchmark that stratifies multi-hop queries by how much intermediate reasoning they expose. Across biomedical KGQA benchmarks, AdaPath consistently outperforms baselines, sustaining meaningful path-finding even when multi-hop queries expose less surface information. The source code is available at https://github.com/Jun-Hyeong-Kim/AdaPath.