{
  "id": 15557460,
  "title": "Beyond Action Prediction: Learning Search-Free Planning in Latent Space",
  "url": "https://urgent.news/2026/10/16/beyond-action-prediction-learning-search-free-planning-in-latent-space",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-16T15:26:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.23615v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Latent world models learn action-conditioned state transitions in a compact representation space, providing a predictive model for visual planning and control. However, planning with these models typically requires costly test-time optimization over action sequences. We propose to replace this online search with a goal-conditioned inverse model that directly predicts actions from current and goal…",
  "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."
}