{
  "id": 19078703,
  "title": "ENGRAM: From Agent Experience to Weights through Verified Consolidation",
  "url": "https://urgent.news/2026/11/06/engram-from-agent-experience-to-weights-through-verified-consolidation",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-06T13:51:02.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.09472v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Language-model agents use experience through external retrieval and parameter learning. Consolidating stored experience into model weights requires deciding both which experience to train on and when its external records can leave retrieval. Admission based solely on student mastery can exclude useful teacher demonstrations, while removal immediately after training can discard guidance that the…",
  "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."
}