{
  "id": 19301759,
  "title": "Patching Up Circuit Learning via Parameter Sharing",
  "url": "https://urgent.news/2026/11/09/patching-up-circuit-learning-via-parameter-sharing",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-09T13:59:16.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.12436v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Probabilistic circuits (PCs) have emerged as generative models guaranteeing tractable probabilistic inference. However, tractability comes at the price of larger model sizes than in other generative models. As such, to scale PCs to high-dimensional data such as images, previous work has devised several parameter sharing techniques as well as fast evaluation protocols that consider learning PCs on…",
  "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."
}