{
  "id": 19078705,
  "title": "From LoRA to Language: Recovering Skill Documents from Adapters",
  "url": "https://urgent.news/2026/11/06/from-lora-to-language-recovering-skill-documents-from-adapters",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-06T13:26:43.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.09454v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Agent skills are short documents that teach a language model how to handle a class of tasks. To save context, these documents are increasingly compiled into low-rank adapters (LoRA), which keep the behaviour but lose the text: an adapter is hard to read, edit, audit or move to another model. We ask whether the skill document can be written back from the adapter and its frozen base model, and we…",
  "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."
}