{
  "id": 7486880,
  "title": "Recurrent GraphNeural NetworkswithSet-BasedAggregation",
  "url": "https://urgent.news/2026/09/14/recurrent-graphneural-networkswithset-basedaggregation",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-14T17:39:41.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.15932v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Recurrent GNNs iterate message passing to convergence, and their logical characterizations to date rely on multi-set aggregation, graded (counting) logics, and halting or acceptance conditions that cannot be verified from the network's parameters. We study recurrent GNNs with set-based aggregation and identify sufficient conditions checkable from the weights for networks to compile into formulas…",
  "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."
}