论文提出了RecipeNet，专门处理材料合成、制药配方这类结构化步骤数据，比通用表格模型更能捕捉层级和顺序信息。
RecipeNet是一种层级Transformer架构，用于编码配方数据中步骤内的字段交互和步骤间的顺序依赖。实验在多个配方数据集和任务上进行，结果显示RecipeNet持续优于现有表格模型，证明了层级与顺序建模对配方表示学习的价值。该研究来自arXiv论文2608.14505v1。
RecipeNet: A Hierarchical Transformer for Recipe Data
Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting their ability to capture hierarchical field interactions and procedural dependencies. We propose RecipeNet, a hierarchical Transformer architecture that encodes field-level interactions within each step and sequential dependencies across steps through stacked Transformer encoders. Experiments on multiple recipe datasets and tasks demonstrate that RecipeNet consistently outperforms existing tabular models, highlighting the value of hierarchical and sequential modeling for recipe representation learning.