{
  "id": 6702090,
  "title": "From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge",
  "url": "https://urgent.news/2026/09/10/from-parameters-to-answers-how-llms-retrieve-and-use-their-internal",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-10T17:39:55.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.11859v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "How does a language model's dependence on query-routing information and target knowledge change as it answers a question? We study this question through layerwise interventions on the hidden state at the end of the question. Across Qwen, Llama, and Gemma, we compare country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct. A…",
  "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."
}