ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Video

ProactEgoMem is a benchmark for evaluating proactive memory in streaming egocentric video, focusing on three tasks—Information Capture, State Tracking, and Location Tracking—to reflect real-world environmental needs. It introduces two levels of proactiveness: Notification Level, which requires a single timely alert, and Intervention Level, which demands continuous memory maintenance via an active tool lifecycle. The benchmark includes daily and scientific datasets with corresponding QA sets, and shows that current models struggle to capture memory both timely and accurately.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

• agents
• multimodal
• benchmarks