Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving when-to-speak decision quality in online streaming scenarios.

翻译：近期视频大语言模型（Video-LLMs）的进展已实现对长复杂视频的强离线推理能力。然而，实际部署日益要求流式感知与主动交互能力，即视频帧在线到达时，系统不仅需要决定回答什么，还需判断何时作答。本研究受流式视频中时域过渡会自然形成跨度结构激活模式的观测启发，将流式视频中的主动激活问题重新定义为结构化序列建模任务。为捕捉这类跨度级结构，我们在滑动时域窗口上联合建模激活信号，并随新帧到达迭代更新。为此提出STRIDE（结构化时序迭代去噪优化），该方法在激活接口处嵌入轻量级掩码扩散模块，用于在窗口内联合预测并逐步优化激活信号。在多种流式基准测试与下游模型上的广泛实验表明，STRIDE能生成更可靠且时序连贯的主动响应，显著提升在线流式场景中"何时发言"的决策质量。