做科研假设生成或理论驱动建模的研究者，可以用这个管道从文献中自动挖掘新假设，比手动推导更系统高效，值得在数据科学之外的其他学科试试。
DN-Hypo-Pipeline 是一个基于大语言模型的AI工作流，旨在通过利用科学解释作为先验知识，辅助研究人员从现有文献中推导出新颖的研究假设。该管道从论文的结论（explanandum）出发，识别其背后的定律、理论和原理，并重构出对观察现象的新解释。在数据科学建模领域的评估中，结合LLM裁判和人类专家评价，该管道比直接生成方法更有效。此外，两个得分最高的生成假设被转化为新算法，性能超过了原论文的基线模型。该方法本质上是理论引导建模的泛化，有望扩展到其他科学领域。
DN-Hypo-Pipeline: An AI-Driven Workflow for Hypothesis Generation via Large Language Models and Scientific Explanations
A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning about scientific phenomena. We introduce DN-Hypo-Pipeline, an AI-powered workflow based on large language models, designed to support structured scientific thinking and hypothesis generation by leveraging scientific explanations as prior knowledge. This pipeline assists researchers in deriving novel hypotheses from existing literature. Given the explanandum (i.e., the conclusion) of a research paper, it identifies underlying laws, theories, and principles, and reconstructs a new, yet-to-be-verified explanation for the observed phenomenon. We evaluated DN-Hypo-Pipeline in the field of data science modeling using three highly cited papers. Statistical inference, supported by both LLM-as-judge assessment and human expert evaluation, demonstrates that our pipeline is more effective than direct generation methods. Additionally, we validated the two highest-scoring generated hypotheses by developing corresponding novel algorithms, which outperformed the baseline models presented in the original papers. Beyond application in data science, DN-Hypo-Pipeline provides a theoretical framework that not only encompasses theory-guided data science modeling methods but also reveals a more fundamental structure of the modeling process. Moreover, this approach is essentially a generalization of theory-guided modeling, offering potential for extension to other domains and across a broader range of scientific disciplines.