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.

翻译：构建个性化心脏电生理数字双胞胎需要为每位患者识别合适的模型结构，而不仅仅是拟合参数。传统方法依赖专家手动设定混合物理-神经架构，这需要深厚的领域专业知识且无法跨患者迁移。近期研究已应用大型语言模型生成或充当混合模型，但尽管这些基于LLM的方法具有强大的泛化能力，它们缺乏心脏模拟所需的稳定结构先验。为此，我们提出LEADS框架，将心脏电生理领域知识形式化为结构化动作空间，并利用LLM智能体发现混合模型。该智能体遵循迭代推理-行动循环，选择、组合并优化混合模型，同时梯度下降处理参数拟合。所提出的LEADS框架使每个候选模型都具备物理基础、可解释性和数值稳定性，同时允许开放式架构探索。我们在含三种真实反应模型的合成数据及真实心脏电生理数据上验证了LEADS，结果表明其性能优于人工设计的混合模型及其他基于LLM的混合建模方法。