{
  "id": 9676799,
  "title": "Learning to Ideate for Scientific Impact",
  "url": "https://urgent.news/2026/09/24/learning-to-ideate-for-scientific-impact",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-24T13:37:59.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.29802v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Scientific ideation is increasingly mediated by large language models, but current ideation systems are usually trained and evaluated on immediately judgeable proxies such as novelty, clarity, and feasibility. This leaves open whether delayed signals of scientific uptake can be used as feedback for steering models toward research directions with higher expected \\emph{impact}. We study this…",
  "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."
}