{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreiczvnrgcmek5bm7zekhkmabs5jfquclz7aznyvzonjykpmxkkdqny",
    "uri": "at://did:plc:2gbt2dlwaqovtnmxkat3tyke/app.bsky.feed.post/3mklyki54zif2"
  },
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreigkftinjphtmhja7pvej56um4bs7cgj6rzwiyjsqtne4rakiw63fu"
    },
    "mimeType": "image/jpeg",
    "size": 84849
  },
  "path": "/articles/d41586-026-00820-5",
  "publishedAt": "2026-04-29T01:54:29.850Z",
  "site": "https://www.nature.com",
  "tags": [
    "doi:10.1038/d41586-026-00820-5"
  ],
  "textContent": "Nature, Published online: 28 April 2026; doi:10.1038/d41586-026-00820-5\n\nTraining AI world models on data about physical environments could improve their real-world capabilities in technologies such as robotics.",
  "title": "‘World models’ are AI’s latest sensation: what are they and what can they do?",
  "updatedAt": "2026-04-28T00:00:00.000Z"
}