{"slug": "deeptcm1-0-a-multi-expert-ai-agent-for-deciphering-mechanisms-of-chinese-herbal", "title": "DeepTCM1.0: A Multi-Expert AI Agent for Deciphering Mechanisms of Chinese Herbal Formulae Based on General Large Language Models", "summary": "Researchers introduced DeepTCM1.0, a multi-expert AI agent framework built on the DeepSeek V3.2 large language model, to decipher mechanisms of traditional Chinese medicine (TCM) compound formulas. The framework uses a three-tier collaborative architecture and an 11-agent simulation to analyze Guizhi Decoction, with performance validated through 100 independent scoring assessments from four LLM evaluators. This addresses limitations of conventional methods and general LLMs in TCM mechanistic analysis.", "body_md": "arXiv:2608.18103v1 Announce Type: new\nAbstract: Background: Mechanistic elucidation of traditional Chinese medicine (TCM) compound formulas remains a central challenge in the modernization of TCM. Conventional approaches, including data mining and network pharmacology, are insufficient for achieving deep integration between classical TCM theory and modern scientific research. In addition, direct question-answering using general-purpose artificial intelligence large language models is limited by inadequate adaptation to TCM theoretical frameworks and susceptibility to reasoning hallucinations. Consequently, there is an urgent need to develop intelligent analytical methods aligned with the holistic principles of TCM. Objective: To establish a multi-expert intelligent agent framework integrating classical TCM theory with modern life sciences, thereby enabling systematic and interpretable mechanistic analysis of TCM compound formulas, with Guizhi Decoction serving as a representative validation case. Methods: The DeepTCM1.0 framework was constructed based on the general-purpose large language model DeepSeek V3.2. It adopts a three-tier collaborative architecture and a three-round iterative quality-control workflow, simulating the collaborative analytical process of 11 interdisciplinary intelligent agents. The framework was applied to the mechanistic interpretation of Guizhi Decoction from the dual perspectives of classical traditional Chinese medicine theory and modern scientific research. Framework performance was comprehensively evaluated through double-blind five-dimensional scoring, intraclass correlation coefficient (ICC) reliability testing, Mann-Whitney U tests, and effect size analysis. The evaluation employed four independent large language models as evaluators, each conducting five rounds of repeated scoring on five anonymized reports, resulting in a total of 100 independent scoring assessments.", "url": "https://wpnews.pro/news/deeptcm1-0-a-multi-expert-ai-agent-for-deciphering-mechanisms-of-chinese-herbal", "canonical_source": "https://arxiv.org/abs/2608.18103", "published_at": "2026-08-20 04:00:00+00:00", "updated_at": "2026-08-20 04:13:07.965324+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["DeepTCM1.0", "DeepSeek V3.2", "Guizhi Decoction"], "alternates": {"html": "https://wpnews.pro/news/deeptcm1-0-a-multi-expert-ai-agent-for-deciphering-mechanisms-of-chinese-herbal", "markdown": "https://wpnews.pro/news/deeptcm1-0-a-multi-expert-ai-agent-for-deciphering-mechanisms-of-chinese-herbal.md", "text": "https://wpnews.pro/news/deeptcm1-0-a-multi-expert-ai-agent-for-deciphering-mechanisms-of-chinese-herbal.txt", "jsonld": "https://wpnews.pro/news/deeptcm1-0-a-multi-expert-ai-agent-for-deciphering-mechanisms-of-chinese-herbal.jsonld"}}