不要等待回复：通过主动思考实现响应迅速且深思熟虑的对话
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
AI总结：
研究针对大语言模型被动思考导致对话延迟问题，提出主动思考框架，介绍无训练基线，通过模拟实时对话流基准测试，证明该方法提升交互效率且不损性能。
摘要：
思考已成为大语言模型处理复杂任务的关键能力。但其被动反应性会引入延迟。本文提出主动思考框架，让模型在对话间隙预计算潜在回复元素，引入无训练基线，通过模拟实时对话流基准测试，证明主动思考有效提升交互效率且不影响性能，倡导向更智能、实时的对话式人工智能转变。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
