{
  "id": 1403370,
  "title": "RecipeNet: A Hierarchical Transformer for Recipe Data",
  "url": "https://urgent.news/2026/08/14/recipenet-a-hierarchical-transformer-for-recipe-data",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-14T17:18:36.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.14505v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}