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

TL;DR

StreamReady introduces a timing-aware approach for streaming video understanding, enabling models to answer questions precisely when sufficient evidence appears, improving real-time accuracy and responsiveness.

Contribution

It proposes the ARS metric and the StreamReady framework, unifying temporal reasoning with on-time answering in streaming videos, along with a new benchmark ProReady-QA.

Findings

StreamReady outperforms prior methods on ProReady-QA.

ARS improves measurement of answer timing accuracy.

Framework generalizes across multiple long-video benchmarks.

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.