这篇论文用LLM智能体自动设计心脏数字孪生的混合模型，比人工靠经验搭的更准，还跨病人管用。合成和真实数据上都赢了其他方法。
论文提出LEADS框架，利用LLM智能体在结构化动作空间中迭代发现混合物理-神经模型，解决传统方法需要专家手动设计且无法跨患者迁移的问题。在三种合成反应数据和真实心脏电生理数据上，LEADS均优于人工设计的混合模型和其他基于LLM的方法。该方法保证了模型的物理合理性、可解释性和数值稳定性，同时允许开放性的架构探索。
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters. Traditional methods rely on experts to manually prescribe hybrid physics-neural architectures, which requires deep domain expertise and does not transfer across patients. Recent works have applied large language models (LLMs) to generate or act as hybrid models. However, despite their promising generalization capacity, these LLM-based methods lack the structural priors needed for stable cardiac simulations. Hence, we propose LEADS, a framework that formulates cardiac EP domain knowledge as a structured action space and utilizes an LLM agent to discover hybrid models. The agent follows an iterative reasoning-and-action loop to select, combine, and refine hybrid models, whilst gradient descent handles parameter fitting. The proposed LEADS designs every candidate model towards physically grounded, interpretable, and numerically stable, while allowing open-ended architectural discovery. We validate LEADS on synthetic data with three ground-truth reaction models and on real cardiac EP data, demonstrating that it outperforms both human-designed hybrid models and other LLM-based hybrid modeling.