{
  "id": 15702443,
  "title": "TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning",
  "url": "https://urgent.news/2026/10/19/tokengate-adapting-llms-for-fast-and-memory-efficient-prefill-with",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-19T17:15:53.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.26754v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Expert human readers process large inputs (e.g., code repositories and long documents) by skimming to closely read only what matters. On the other hand, language models process all input tokens through each model layer, resulting in significant processing costs for long-context inputs. We introduce TokenGate, a small, drop-in component that extends existing trained models to enable end-to-end…",
  "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."
}