arXiv AI
AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 25
SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning
arXiv:2608.22132v1 Announce Type: cross Abstract: Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and...
arXiv AI
Oct 27
Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation
The paper introduces ParallaxRAG, a multi-view retrieval-augmented generation framework that addresses multi-hop reasoning challenges in large language models over knowledge graphs. It identifies that transformer attention heads specialize in distinct semantic relations across reasoning stages, forming a hop-aligned relay pattern, and proposes decoupling queries and KGs into head-specific semantic spaces to enforce relational diversity. Experiments on WebQSP, CWQ, and BioASQ show state‑of‑the‑art performance, reduced hallucination, and strong generalization.
arXiv Computation and Language
4d ago
MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
MedResearcher‑R1 is a medical deep‑research agent that uses a Knowledge‑Informed Trajectory Synthesis (KISA) framework to build medical knowledge graphs and generate complex multi‑hop question‑answer pairs, especially for rare medical entities. It also incorporates a medical retrieval engine and general‑purpose tools to reliably synthesize medical evidence. The resulting 2,100+ trajectories across 12 specialties enable supervised fine‑tuning and reinforcement learning, producing a 32B model that outperforms larger proprietary systems on MedBrowseComp and shows strong performance on GAIA and xBench benchmarks.
arXiv AI
Jun 9
Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
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.
arXiv AI
Sep 10
DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing
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...
arXiv AI
Aug 10
MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring
arXiv:2608. 06713v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unregistered molecules disconnected.