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arXiv · 2604.15037

From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench

Ke Xu·Yuhao Wang·Yu Wang
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Abstract

Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existing benchmarks primarily focus on reactive responses, overlooking the complexities of proactive intervention and monitoring. To bridge this gap, we introduce ProVoice-Bench, the first evaluation framework specifically designed for proactive voice agents, featuring four novel tasks. By leveraging a multi-stage data synthesis pipeline, we curate 1,182 high-quality samples for rigorous testing. Our evaluation of state-of-the-art Multimodal LLMs reveals a significant performance gap, particularly regarding over-triggering and reasoning capabilities. These findings highlight the limitations of current models and offer a roadmap for developing more natural, context-aware proactive agents.

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Ke Xu, Yuhao Wang, Yu Wang. 2026-04-16. From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench. https://arxiv.org/abs/2604.15037

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