TCMScience: An Autonomous Scientist for Traditional Chinese Medicine

arXiv 2026 65.3 method

TLDR

TCMScience is an autonomous TCM knowledge-discovery agent that separates language-model proposals from execution, uses claim-support contracts, and evaluates network-pharmacology workflows.

评分理由

The paper introduces a principled architecture for autonomous scientific discovery with extensive resource catalogs and a concrete case study, which are strengths. However, the abstract is truncated and lacks full quantitative results and explicit limitations, making the empirical evidence incomplete.

Read-first 评分解释

综合优先阅读分 65.3，由主题、引用、图谱、方法、可复现性和近期性等信号加权得到。原始总分保留为 72。

近期性 8%

100

使用温和的时间衰减，让近期论文更容易浮现，同时保留较早基础工作的价值。年份：2026

方法质量 25%

检查可见的摘要与分析字段，寻找实验、数据集、基线、指标和局限性等方法证据。命中信号：消融、分析、数据集、评估、结果

主题相关性 42%

使用现有 LLM 关键词相关性评分，并归一化到 0-100。关键词：AI scientist、automated scientific discovery、autonomous research agent、automated research、literature review agent、survey generation、automated experimentation、experiment design agent、AI for scientific research、paper writing agent、research automation、scientific discovery agent

可复现性 25%

检查链接和可见文本中的论文、代码、数据集、工件与仓库信号。论文：有；代码：无；数据：无；命中信号：数据集

研究版图角色

前沿论文方法锚点

排序敏感性

稳定性：volatile；排名波动范围：25。

关键词评分

autonomous research agent

automated scientific discovery

AI for scientific research

scientific discovery agent

AI scientist

research automation

automated research

automated experimentation

experiment design agent

literature review agent

survey generation

paper writing agent

深度分析

创新点

Separation of language-model proposals from execution and release authority
Architecture combining a trusted kernel, a scientific compiler, and claim-support contracts
Compilation of plans into typed operations with explicit execution permissions and pre-release checks for source provenance, study design, and claim scope
Large model-independent resource layer: 117 public-source connectors with 444 typed operations across 29 domains, 150 native tools across 12 domains, and a TCM data hub with 136 databases, 76 dataset specifications, and 84,294 formula-source records
Layered governance that jointly reduces release of erroneous outputs

方法

TCMScience uses a trusted kernel, scientific compiler, and claim-support contracts to compile language-model plans into typed operations, bound execution by explicit permissions, and check candidate findings against source provenance, study design, and claim scope before release. It integrates a capability registry of 117 public-source connectors with 444 typed operations across 29 domains, 150 native tools across 12 domains, and a TCM data hub cataloging 136 databases, 76 dataset specifications, and 84,294 formula-source records, supporting browser and local execution interfaces. Evaluations include a network-pharmacology analysis of Gegen Qinlian Tang and a governance ablation over 121 constructed erroneous outputs spanning 26 classes plus 10 compliant controls.

关键结果

In network-pharmacology analysis, replacing the whole-proteome background with the assayed-protein background changed significant pathways from 66 to zero, while incorporating inactive screening records identified nine pathways including drug-metabolism signals retained as mechanistic hypotheses. In the governance ablation, 121 errors were released without governance, 57 with the domain layer, 7 with the kernel, and 0 with both layers; all 10 compliant controls were retained.

资源

论文