{
  "id": 17925876,
  "title": "D&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models",
  "url": "https://urgent.news/2026/11/02/d-a-decoupling-description-and-assessment-enables-vlms-as-zero-shot",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-02T18:52:11.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.03622v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks frequently suffer from confirmation bias, hallucinating progress in failed executions due to implicit success assumptions or relative temporal ranking designs. Naive prompting of frontier models collapses into a binary success detector that fails to capture nuanced,…",
  "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."
}