{
  "id": 2279129,
  "title": "Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization",
  "url": "https://urgent.news/2026/08/20/inject-align-recover-staged-post-training-for-retrieval-free-document",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-20T17:14:24.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.20281v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large language models often fail to answer questions about a bounded document collection when the source documents are not retrieved at inference time. We study this setting as document knowledge internalization: converting a fixed corpus into usable parametric knowledge for retrieval-free question answering. We propose IAR (Inject, Align, and Recover), a three-stage post-training framework that…",
  "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."
}