R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video

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 clip retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-moti

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ethics.ai (11 August 2026), “R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video,” evidence record 18407, https://ethics.ai/record/18407 (originally published by arXiv).

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