DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts

Zijian Carl Ma, Sean J. Wang, Sijbren Manuel Kramer, Li Erran Li

Keywords: AI, Multi-Agent LLM, Knowledge Graph, KG-Augmented LLMs, Drug Discovery, Biomedical Knowledge Graphs

TL;DR: KGs ground, LLMs reason, and their combination makes biological discovery tractable when ground truth is sparse and only partially observed.

Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development. However, pre-ontological prose and idiosyncratic taxonomies prevent the standardization and medical modernization of the data for use in current biomedical pipelines. Furthermore, no existing LLM agent system, whether tool-calling, retrieval-augmented, or agentic deep-research, can convert such text into verifiable drug-discovery leads at scale. We close this gap with DeepRoot, a multi-agent LLM system that jointly builds and utilizes a verified knowledge graph, showing that grounding and reasoning often conflated are separable axes the system can compose for therapeutic reasoning. Applied to the Shen Nong Ben Cao Jing, DeepRoot recovers 10 of 21 held-out compound-disease treatment pairs at R@20 (47.6% vs. 4.8% for a raw corpus LLM and ~2.4% random) and dominates an LLM-as-judge audit for reasoning quality over baseline LLMs and LLMs with direct tool-call access to the same APIs DeepRoot itself queries. Tool-using LLMs including Biomni, a specialized biomedical agent hallucinate evidence on 87% of claims, versus 7-10% for DeepRoot. Graph-only inference hallucinates 0% but ranks lowest on reasoning coherence; DeepRoot KG + LLM is the only condition to win on both axes, pointing toward a route for systematic mining and repurposing of historical medical knowledge.

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Submission Number: 205