Proact-VL enables real-time AI companions that know when and how much to speak.
Proactive and real-time interactive experiences are essential for human-like AI companions, yet face three key challenges: (1) achieving low-latency inference under continuous streaming inputs, (2) autonomously deciding when to respond, and (3) controlling both quality and quantity of generated content to meet real-time constraints. In this work, we instantiate AI companions through two gaming scenarios, commentator and guide, selected for their suitability for automatic evaluation. We introduce the Live Gaming Benchmark, a large-scale dataset with three representative scenarios: solo commentary, co-commentary, and user guidance, and present Proact-VL, a general framework that shapes multimodal language models into proactive, real-time interactive agents capable of human-like environment perception and interaction. Extensive experiments show Proact-VL achieves superior response latency and quality while maintaining strong video understanding capabilities, demonstrating its practicality for real-time interactive applications.
Proact-VL balances proactive response timing with low-latency generation for real-time AI companions in gaming.
The Live Gaming Dataset provides 561 hours of professional commentary across twelve games for training and evaluation.
A lightweight FLAG token mechanism autonomously decides when to respond based on visual and contextual cues.
Evaluation focuses on gaming scenarios; generalization to other real-time interactive applications remains unclear.
Script cuts off mid-explanation of the proactive mechanism; complete technical details are unavailable.
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