Thinking While Speaking: Inference-Time Knowledge Transfer for Responsive and Intelligent Conversational Voice Agents
arXiv:2511.07397v2 Announce Type: replace Abstract: Voice agents face a fundamental tension: the reasoning, retrieval, and tool use that make foundation models capable are iterative and slow, while conversational interaction demands responses on a millisecond timescale. Smaller, real-time models meet the latency bar but cannot match foundation models on complex tasks, leaving current voice agents to trade away either responsiveness or capability. We introduce conversational infill, where a small talker model both immediately generates contextually grounded responses to hide the latency of an e
The rapid advancement of large foundation models has highlighted their latency issues in real-time conversational contexts, necessitating immediate solutions to bridge the gap between capability and responsiveness.
This development addresses a core tension in AI — balancing the power of complex models with the millisecond response times required for natural human-computer interaction, directly impacting user experience and application viability.
Voice agents can now offer both high capability and real-time responsiveness, potentially making them more integrated and effective in critical environments where both speed and intelligence are paramount.
- · AI voice agent developers
- · Customer service industries
- · Consumers of voice AI
- · Foundational AI model providers
- · Providers of latency-prone voice AI systems
- · Companies unable to integrate complex inference-time solutions
Immediate improvement in the user experience of AI-driven conversational interfaces.
Accelerated adoption of voice AI across various sectors due to enhanced performance and usability.
Increased reliance on sophisticated AI systems for real-time decision-making and interaction, blurring lines between human and AI communication.
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