{
  "id": 6088849,
  "title": "ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs",
  "url": "https://urgent.news/2026/09/04/ace-adaptive-calibration-free-expert-skipping-for-moe-based-llms",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-04T14:56:00.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.05228v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs), yet fixed top-k routing activates the same number of expert slots for every token, causing substantial redundant computation. Existing expert-skipping methods often rely on router confidence, calibration data, or additional training, and therefore cannot reliably estimate the actual…",
  "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."
}