Proactive Thinking framework reduces latency in LLM dialogue

The paper introduces Proactive Thinking, a framework that allows Large Language Models to pre-compute potential response elements during conversational downtime rather than waiting idly for user input. This approach aims to bridge the gap between reactive AI reasoning and human-like anticipatory dialogue.

• The method employs a training-free baseline that anticipates future states through speculative continual thinking.
• Evaluation utilizes three adapted benchmarks configured into time-aware environments to simulate real-time conversational flow.
• Results demonstrate that proactive thinking improves interaction efficiency without compromising performance.

The authors advocate for this shift toward intelligent, anticipatory AI as a fundamental improvement for real-time conversational systems.