ProactiveLLM: Learning Active Interaction for Streaming Large Language Models

Abstract

Standard Large Language Models (LLMs) operate on a ''read-then-generate'' paradigm, incurring avoidable latency and computational redundancy. Recently, streaming LLMs have attempted to overcome these bottlenecks by allowing input and output to unfold synchronously. However, this introduces a critical challenge: how should the LLM determine the optimal timing to interact with the input and output stream? Existing approaches remain confined to *passive adaptation*, relying on static or content-irrelevant interaction rules. In this paper, we propose *ProactiveLLM*, which achieves active interaction by treating ''when to generate" and ''what to generate" as decoupled objectives. Through masked streaming modeling and self-distillation, the model actively learns to perceive semantic sufficiency from partial inputs. This yields endogenous cues serving as a versatile interface for the plug-and-play integration of diverse decision heads customized for specific latency-accuracy trade-offs. Extensive evaluation across text and speech streaming tasks confirms that ProactiveLLM significantly reduces interaction latency while maintaining quality, validating its capacity for dynamic and active interaction.