Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

2026-07-13

AI Summary

Existing intelligent assistants struggle to deliver timely proactive assistance in continuous first-person videos by effectively integrating user history, current activities, and optimal intervention timing. This work formulates proactive assistance as a context-dependent decision-making problem and introduces EgoMemo, a memory-augmented agent that fuses multi-scale temporal summaries, semantic knowledge graphs, and visual embedding archives to enable training-free, real-time context-aware decisions. To facilitate research in this direction, we present EgoServe, the first benchmark for proactive assistance in continuous first-person video. Experiments demonstrate that EgoMemo establishes a strong baseline on EgoServe while maintaining competitive performance on existing first-person video tasks, validating both its effectiveness and generalization capability.

Abstract

When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive. Yet existing approaches either wait passively for user queries or treat every detected event as requiring a response, without considering the user's history, current activity, or whether assistance would actually be welcome. We reframe proactive assistance as a context-dependent decision problem: the agent must not only perceive what is happening, but reason over accumulated temporal context to determine when and whether to intervene. To this end, we present Vinci2, a proactive egocentric assistance system that advances the on-device assistant Vinci from reactive response toward proactivity. On the evaluation side, we present EgoServe, the first large-scale benchmark for proactive assistance in continuous egocentric video. EgoServe comprises over 3,000 service instances organized along 4 temporal memory horizons, ranging from immediate safety alerts to long-term habit coaching, across 10 service categories. On the modeling side, we propose EgoMemo, a training-free, memory-augmented agent that maintains three complementary memory representations: multi-scale temporal summaries, a semantic knowledge graph, and visual embedding archives. At each timestep, EgoMemo performs retrieval-augmented reasoning to determine whether assistance is warranted and, if so, produces contextually grounded responses. Experiments demonstrate that EgoMemo establishes strong baselines on EgoServe while remaining competitive on existing egocentric benchmarks. Our benchmark and code are publicly available at \href{https://sitonggong.github.io/EgoServe-page/}{Vinci2}.

Problem

• proactive assistance
• egocentric video
• context-aware intervention
• temporal context
• intelligent assistant

Innovation

• proactive assistance
• egocentric video
• memory-augmented agent
• retrieval-augmented reasoning
• temporal context