{
  "id": 18258982,
  "title": "Learning to Decode Linear Codes with Generalized Belief Propagation Network",
  "url": "https://urgent.news/2026/11/04/learning-to-decode-linear-codes-with-generalized-belief-propagation",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-04T18:29:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.06874v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms. In this paper, we propose a Generalized Belief Propagation Network (GBPNet) that preserves message-passing symmetry while enhancing the decoder's learning…",
  "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."
}