{
  "id": 4757557,
  "title": "Wide Learning: Learning to Reach Evidence",
  "url": "https://urgent.news/2026/08/30/wide-learning-learning-to-reach-evidence",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-30T06:58:50.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.29608v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Machine learning is usually evaluated after an evidence interface has been fixed. A dataset, sensor suite, query language, action set, or experimental protocol determines which observations can be obtained, and learning is judged by what it extracts from them. We study a complementary capability. A learner's state can determine which evidence-generating experiments it can reliably realise under…",
  "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."
}