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DeepRoot system extracts therapeutic insights from historical medical texts

Researchers have developed DeepRoot, a novel multi-agent LLM system designed to extract therapeutic insights from historical medical texts. This system constructs and utilizes a verified knowledge graph to improve grounding and reasoning capabilities, addressing limitations in current LLM agent systems for drug discovery. Applied to the Shen Nong Ben Cao Jing, DeepRoot demonstrated significant success in recovering known compound-disease treatment pairs and exhibited superior reasoning quality with substantially lower hallucination rates compared to baseline LLMs and tool-using LLMs. AI

IMPACT This system offers a new approach for mining historical medical knowledge, potentially accelerating drug discovery by reducing LLM hallucination rates.

RANK_REASON The cluster contains an academic paper detailing a new system and its performance on a specific task.

Read on arXiv cs.MA (Multiagent) →

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zijian Carl Ma, Sean J. Wang, Sijbren Kramer, Li Erran Li ·

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

    arXiv:2606.15931v1 Announce Type: cross 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 stan…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Li Erran Li ·

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

    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 …