arXiv:2607.03093v1 Announce Type: cross 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 poten
This development addresses a fundamental limitation in current LLM interaction paradigms, moving closer to human-like conversational fluidity.
Improved responsiveness in LLMs significantly enhances user experience and expands practical applications, making AI systems more naturally integrated into workflows.
LLM interactions will become less disjointed and latency-prone, reducing user friction and enabling more complex, real-time conversational applications.
- · AI software developers
- · Customer service platforms
- · Conversational AI companies
- · Platforms relying on primitive reactive AI
- · Users with high latency tolerance
LLMs will exhibit more natural and less frustrating conversational flows.
The improved fluidity could enable AI to take on more complex, real-time interactive roles currently limited by latency.
This enhancement might accelerate the development and adoption of fully autonomous AI agents in various sectors.
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Read at arXiv cs.AI