{
  "id": 16002013,
  "title": "Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation",
  "url": "https://urgent.news/2026/10/21/think-harder-where-it-matters-item-level-adaptive-recursion-for",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-21T17:58:06.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.30128v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after…",
  "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."
}