{
  "id": 7924120,
  "title": "Higher-order pruning of experts in mixture-of-experts language models",
  "url": "https://urgent.news/2026/09/16/higher-order-pruning-of-experts-in-mixture-of-experts-language-models",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-16T16:55:46.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.18916v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count, yet existing methods make pruning decisions for each expert independently, and assume experts' contributions are purely additive. In reality, expert usage in MoEs is inherently cooperative. We…",
  "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."
}