{
  "id": 1403369,
  "title": "Split the Labor: Separating Evidence Interpretation from Decision Aggregation",
  "url": "https://urgent.news/2026/08/14/split-the-labor-separating-evidence-interpretation-from-decision",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-14T17:24:55.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.14509v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Systems that ask a language model to reach a conclusion from many sources usually concatenate them into one prompt. This conflates two operations with different requirements. Interpreting a source rewards capacity and context. Combining interpretations rewards fixed arithmetic, comparability across instances, and the option to return nothing. Once separated, the design problem becomes the…",
  "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."
}