AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 14 of its 32 references that are in the map. First posted on arXiv 31 Aug 2026.
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14 of 32 references.
References · 14
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2022Large Language Models Encode Clinical Knowledgecited by 1,807
- 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
- 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
- 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
- 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 266
- 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 235
- 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 121
- 2021Can Language Models be Biomedical Knowledge Bases?cited by 83
- 2024Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphscited by 77
- 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 53
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- 2025BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
- 2025K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
- 2026Weaving Multi-Source Evidence for Biomedical Reasoning: The BioMedHop Benchmark and BioWeave Framework
- 2026Shattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMs
- 2026ReGraM: Region-First Knowledge Graph Reasoning for Medical Question Answering
- 2026MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
- 2025Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation
- 2026LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval