Abstract
Streaming video understanding often involves time-sensitive scenarios where models need to answer exactly when the supporting visual evidence appears: answering before the evidence reflects speculation, answering after it has passed reduces real-time utility. To capture this behavior, we introduce a readiness-aware formulation of streaming video understanding with the Answer Readiness Score (ARS), a timing-aware objective with asymmetric early and late penalties. When combined with correctness, ARS defines an effective accuracy that measures not just whether a model is right, but whether it answers at the appropriate moment. Building on this formulation, we introduce StreamReady, a framework to unify temporal reasoning with on-time answering through a lightweight readiness mechanism that decides if sufficient evidence has been observed before responding. To evaluate this capability, we further introduce ProReady-QA, a benchmark with annotated answer evidence windows and proactive multi-turn questions across local and global contexts. StreamReady achieves superior performance on ProReady-QA, and consistently outperforms prior methods across eight additional streaming and offline long-video benchmarks, demonstrating robust and broadly generalizable video understanding capability.
Abstract (translated)
流媒体视频理解通常涉及时间敏感的场景，模型需要在支持性视觉证据出现时准确作答：如果提前于证据出现之前回答，则视为推测；而延迟到证据已过去才作答则降低了实时效用。为捕捉这一行为特征，我们引入了一种具有时间感知目标功能的流媒体视频理解框架——答案准备度得分（ARS），该框架包含不对称的早出和迟答惩罚机制。结合正确性考量后，ARS定义了一个有效准确率指标，不仅衡量模型是否回答正确，还评估其回答时机是否恰当。在此基础上，我们提出了StreamReady框架，它通过轻量级的答案准备度机制统一了时间推理与及时作答功能，决定何时累积足够证据后再作出响应。 为了评估这一能力，我们进一步引入了ProReady-QA基准测试集，该测试集包含标注好的答案依据窗口及跨局部和全局上下文的主动多轮问答。StreamReady在ProReady-QA上取得了卓越的表现，并且在八项额外的流媒体与离线长视频基准测试中均持续优于先前的方法，展示了其稳健而广泛适用的视频理解能力。
URL
https://arxiv.org/abs/2603.08620
