StreamReady: Learning What to Answer and When in Long Streaming Videos

• URL: http://arxiv.org/abs/2603.08620v1
• Date: Mon, 09 Mar 2026 17:02:44 GMT
• Title: StreamReady: Learning What to Answer and When in Long Streaming Videos
• Abstract summary: We introduce a readiness-aware formulation of streaming video understanding with the Answer Readiness Score (ARS)<n>ARS defines an effective accuracy that measures not just whether a model is right, but whether it answers at the appropriate moment.<n>Building on this formulation, we introduce StreamReady, a framework to unify temporal reasoning with on-time answering.
• Score: 28.99829321053373
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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