{
  "id": 13005977,
  "title": "Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models",
  "url": "https://urgent.news/2026/10/08/distilling-routed-3d-privilege-for-spatial-reasoning-in-vision",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-08T17:22:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.12355v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Spatial reasoning remains a persistent weakness of vision-language models (VLMs), because RGB inputs do not directly provide geometric evidence. Existing remedies either inject 3D into the model at inference, paying architecture and latency costs, or train with outcome rewards that supervise only the final answer. Spatial errors originate in perception: a misjudged depth or direction can be…",
  "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."
}