{
  "id": 6523624,
  "title": "LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation",
  "url": "https://urgent.news/2026/09/09/literag-cost-efficient-graph-based-retrieval-augmented-generation",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-09T14:32:07.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.10239v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversized contexts that reduce generation efficiency. We present LiteRAG, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction. On…",
  "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."
}