{
  "id": 17183159,
  "title": "On the Fundamental Limits of Prefill-Based Decoding Length Prediction",
  "url": "https://urgent.news/2026/10/29/on-the-fundamental-limits-of-prefill-based-decoding-length-prediction",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-29T17:36:01.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.40541v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Predicting how many tokens a large language model (LLM) will decode, using only prefill-phase hidden states, is a key primitive for inference scheduling. We show that current benchmarks conflate predictor quality with task hardness: when the target model, dataset, or sampling configuration changes, reported accuracy collapses even though the predictor is unchanged. We trace this instability to an…",
  "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."
}