EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams

Researchers introduce EgoPro-Bench, a comprehensive benchmark dataset with over 14,000 egocentric videos designed to train and evaluate proactive AI assistants that can understand user intent and interact at optimal moments. The work addresses limitations in existing multimodal large language models by enabling personalized, timing-aware interactions rather than purely reactive responses.

EgoPro-Bench represents a meaningful advancement in how AI systems can transition from passive responders to proactive assistants. Traditional multimodal large language models operate reactively—waiting for user queries before engaging. This benchmark tackles the harder problem: teaching models to continuously monitor streaming video feeds, understand implicit user needs within personalized contexts, and intervene at precisely the right moment. The dataset's scale (12,400+ training videos, 2,400 evaluation videos) across 12 domains provides substantial training material for models to learn nuanced behavioral patterns.

The research builds on emerging recognition that proactivity represents the next frontier in human-AI interaction. While previous benchmarks focused narrowly on alert scenarios, EgoPro-Bench introduces simulated user profiles to generate diverse, realistic intentions. This personalization layer is critical—the same action means different things depending on user goals, preferences, and context.

For developers and AI researchers, the proposed "short thinking, better interaction" principle offers practical guidance: allocating limited computational budget before intent recognition improves performance while reducing latency. This addresses real-world deployment constraints where streaming inference demands efficiency.

Industry-wide, this work signals movement toward user-centric AI agents that anticipate needs rather than merely respond to commands. Success in egocentric video understanding could cascade into autonomous systems, wearable assistants, and smart environments. The benchmark enables standardized evaluation, reducing fragmentation in how proactivity research advances. However, the work remains largely academic; commercialization timelines and real-world performance gaps remain uncertain.

• →EgoPro-Bench enables training AI models to proactively assist users by understanding intent from egocentric video streams and timing interactions optimally.
• →The benchmark spans 12 distinct domains with simulated user profiles, addressing the limitation of previous work focused only on alert scenarios.
• →A novel "short thinking, better interaction" principle allocates limited token budgets before intent recognition, improving both performance and latency.
• →The dataset includes 2,400 evaluation and 12,400+ training videos with high-fidelity human-machine interaction annotations.
• →This work establishes foundational benchmarks for next-generation proactive AI agents that understand personalized context rather than reactive-only systems.