Molecular property prediction is fundamental to chemical engineering applications such as solvent screening. We present Socrates-Mol, a framework that transforms language models into empirical Bayesian reasoners through context engineering, addressing cold start problems without model fine-tuning. The system implements a reflective-prediction cycle where initial outputs serve as priors, retrieved molecular cases provide evidence, and refined predictions form posteriors, extracting reusable chemical rules from sparse data. We introduce ranking tasks aligned with industrial screening priorities and employ cross-model self-consistency across five language models to reduce variance. Experiments on amine solvent LogP prediction reveal task-dependent patterns: regression achieves 72% MAE reduction and 112% R-squared improvement through self-consistency, while ranking tasks show limited gains due to systematic multi-model biases. The framework reduces deployment costs by over 70% compared to full fine-tuning, providing a scalable solution for molecular property prediction while elucidating the task-adaptive nature of self-consistency mechanisms.

翻译：分子性质预测是溶剂筛选等化学工程应用的基础。本文提出Socrates-Mol框架，通过语境工程将语言模型转化为经验贝叶斯推理器，无需模型微调即可解决冷启动问题。该系统实现了反思-预测循环：初始输出作为先验，检索的分子案例提供证据，优化后的预测形成后验，从而从稀疏数据中提取可重用的化学规则。我们设计了与工业筛选优先级匹配的排序任务，并采用跨五个语言模型的交叉模型自洽性以降低方差。在胺类溶剂LogP预测实验中观察到任务依赖性规律：回归任务通过自洽性实现72%的平均绝对误差降低与112%的决定系数提升，而排序任务因系统性多模型偏差仅获得有限增益。相较于完整微调，该框架降低超过70%的部署成本，为分子性质预测提供可扩展解决方案，同时阐明了自洽性机制的任务自适应特性。