The integration of Large Language Models (LLMs) into scientific discovery is currently hindered by the Implicit Context problem, where governing equations extracted from literature contain invisible thermodynamic assumptions (e.g., undrained conditions) that standard generative models fail to recognize. This leads to Physical Hallucination: the generation of syntactically correct solvers that faithfully execute physically invalid laws. Here, we introduce a Neuro-Symbolic Generative Agent that functions as a cognitive supervisor atop traditional numerical engines. By encapsulating physical laws into modular Constitutive Skills and leveraging latent intrinsic priors, the Agent employs a Chain-of-Thought reasoning workflow to autonomously validate, prune, and complete physical mechanisms. We demonstrate this capability on the challenge of thermal pressurization in low-permeability sandstone. While a standard literature-retrieval baseline erroneously predicts catastrophic material failure by blindly adopting a rigid "undrained" simplification, our Agent autonomously identifies the system as operating in a drained regime (Deborah number De << 1) via dimensionless scaling analysis. Consequently, it inductively completes the missing dissipation mechanism (Darcy flow) required to satisfy boundary constraints, predicting a stable stress path consistent with experimental reality. This work establishes a paradigm where AI agents transcend the role of coding assistants to act as epistemic partners, capable of reasoning about and correcting the theoretical assumptions embedded in scientific data.

翻译：大型语言模型（LLMs）在科学发现中的应用目前受到“隐式上下文”问题的阻碍：从文献中提取的控制方程包含不可见的热力学假设（例如不排水条件），而标准生成模型无法识别这些假设。这导致“物理幻觉”现象：生成语法正确但忠实执行物理无效定律的求解器。本文提出一种神经符号生成智能体，作为传统数值引擎之上的认知监督器。通过将物理定律封装为模块化的本构技能，并利用潜在内在先验，该智能体采用思维链推理工作流来自主验证、剪枝和补全物理机制。我们在低渗透砂岩的热增压挑战中展示了这一能力。标准的文献检索基线方法盲目采用刚性的“不排水”简化，错误地预测了灾难性的材料失效；而我们的智能体通过无量纲尺度分析，自主识别出系统处于排水状态（德博拉数 De << 1）。因此，它归纳性地补全了满足边界约束所需的缺失耗散机制（达西流动），预测出与实验现实一致的稳定应力路径。这项工作建立了一种新范式：AI智能体超越编码助手的角色，成为能够推理并修正科学数据中嵌入的理论假设的认知伙伴。