{
  "id": 9243506,
  "title": "Canonical locks that encode part-whole hierarchies",
  "url": "https://urgent.news/2026/09/22/canonical-locks-that-encode-part-whole-hierarchies",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-22T11:49:49.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.26046v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "One of the challenges in representational learning is how to encode part-whole hierarchies in a neural net. Prior works rely on flattening tree-like structures into string-like sequences and training a sequence-to-sequence model via autoregression. While such a representation works for parse-trees in NLP, it is not entirely clear how to make it work for images. Thus, we propose a geometric…",
  "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."
}