RecipeNet: A Hierarchical Transformer for Recipe Data

Many real-world processes --- chemical synthesis, drug formulation, manufacturing --- are naturally represented as ordered, structured steps rather than flat tables, yet most machine learning models flatten this structure and lose crucial dependencies. RecipeNet addresses this with a hierarchical Transformer that separately models relationships within each step and dependencies across the full sequence. By outperforming standard tabular learning methods, it offers a general-purpose architecture for procedural or "recipe-like" data. Potential applications include materials science R&D, pharmaceutical formulation optimization, and industrial process modeling wherever step-ordered structured data needs to be learned. Authors: Pin-Yen Huang, Sachin Chhabra, Prasanth Sai Gouripeddi, Abhinav Kumar, Baoxin Li Paper: https://arxiv.org/abs/2608.14505v1