{
  "id": 4570288,
  "title": "How Proper Scoring Rules Shape LLM Forecasting",
  "url": "https://urgent.news/2026/08/28/how-proper-scoring-rules-shape-llm-forecasting",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-28T16:08:51.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.28482v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare five proper scoring rules as training objectives for binary forecasts of resolved real-world events. Although the rules share the same theoretical incentive for truthful probability reporting, the resulting models differ in calibration, probability use, and estimated profiles of…",
  "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."
}