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VoxMind drastically improves task completion rates in spoken dialogue agents, jumping from 34.88% to 74.57%, even surpassing Gemini-2.5-Pro, by integrating "Think-before-Speak" reasoning and asynchronous tool management.
Fine-grained reward signals for semantic quality and interaction timing unlock more human-like spoken dialogue models.
Reinforcement learning can now be practically applied to spoken dialogue models thanks to a new post-training recipe that disentangles semantic and acoustic improvements.
WavBench exposes the limitations of current spoken dialogue models in handling real-world conversational nuances like colloquialisms and paralinguistics, despite advances in reasoning capabilities.
