Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking

Researchers propose Proactive Thinking, a framework enabling LLMs to pre-compute responses during conversational pauses, reducing latency. A training-free baseline using speculative continual thinking improves interaction efficiency without sacrificing quality, tested on time-aware benchmarks. The work advocates for anticipatory, real-time conversational AI.

arXiv:2607.03093v1 Announce Type: new Abstract: Thinking has emerged as a critical capability for Large Language Models LLMs tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upon receiving a user response, inevitably introduces latency that compromises conversational fluidity. This stands in sharp contrast to human dialogue, where speakers proactively anticipate and plan future content during natural pauses to ensure seamless interaction. To bridge this gap, we propose Proactive Thinking, a framework that empowers models to pre-compute potential response elements during conversational downtime instead of waiting idly for the next input. We then introduce a training-free baseline that can think ahead by anticipating future states, balancing efficiency and quality through speculative continual thinking. To evaluate this approach in practice, we adapt three benchmarks of varying complexity into time-aware environments that simulate real-time conversational flow. We demonstrate that proactive thinking effectively improves interaction efficiency without compromising performance. Ultimately, this work advocates for a fundamental shift toward more intelligent, anticipatory, and real-time conversational AI.