From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench
For researchers developing proactive voice agents, this benchmark identifies critical gaps in current models, providing a roadmap for improvement.
ProVoice-Bench introduces the first evaluation framework for proactive voice agents, revealing that current Multimodal LLMs struggle with over-triggering and reasoning, achieving only moderate performance on four novel tasks.
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.