{
  "id": 18075445,
  "title": "Adversarial Speculative Reasoning Improves Long Horizon Agents",
  "url": "https://urgent.news/2026/11/03/adversarial-speculative-reasoning-improves-long-horizon-agents",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-03T18:45:23.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.05345v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Speculative decoding has been treated purely as a systems optimization that speeds up large language model decoding without changing the model behavior. Under this view, the growing effort to train better draft models has been focused solely on acceptance rate. We argue that this view does not hold true for long-horizon agent reasoning. Measuring frontier open-weight models on complex agentic…",
  "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."
}