{
  "id": 625102,
  "title": "R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video",
  "url": "https://urgent.news/2026/08/11/r4dsg-relative-4d-scene-graph-memory-for-object-centric-question",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-11T15:00:15.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.11017v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based memories rarely preserve persistent object identity or structured spatial change. Existing long-video QA methods mainly emphasize temporal grounding and…",
  "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."
}