{
  "id": 158353,
  "title": "When and Where to Look: Adaptive Visual Evidence Scheduling for Efficient Long Video Understanding",
  "url": "https://urgent.news/2026/08/04/when-and-where-to-look-adaptive-visual-evidence-scheduling-for",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-04T16:49:53.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.03918v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Efficient long-video understanding requires vision--language models (VLMs) to reason over a small number of frames selected as sparse visual evidence. Existing relevance-based methods rely on static one-shot selection with fixed frame budgets and candidate pools, while agent-based schedulers achieve adaptivity through costly multi-round reasoning and interactive search. We propose EcoFrame, a…",
  "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."
}