AskStream: When and What to Ask Proactively under Uncertainty in Streaming Video Understanding
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper argues that proactive video-understanding models should gain a third action, ask, alongside wait and answer, so they can request missing information when video or user input is incomplete. To study this, the authors introduce AskStream, a multi-turn benchmark with four task categories and eight subtasks, containing 438 videos and 2,714 manually annotated frame-level time windows labeled wait, ask, or answer. Even the strongest evaluated model reaches only 52.18% on the proposed Ask-F1 metric across multiple-choice and open-ended settings.
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- Primary sourceHugging Face Daily Papers
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The paper argues that proactive video-understanding models should gain a third action, ask, alongside wait and answer, so they can request missing information when video or user input is incomplete. To study this, the authors introduce AskStream, a multi-turn benchmark with four task categories and eight subtasks, containing 438 videos and 2,714 manually annotated frame-level time windows labeled wait, ask, or answer. Even the strongest evaluated model reaches only 52.18% on the proposed Ask-F1 metric across multiple-choice and open-ended settings.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
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- 01Who is making each claim?
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- 02What was actually tested?
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- 03Where are the sources?
Original link: huggingface.co. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; the benchmark's annotation process and Ask-F1 definition are described only at a high level in the supplied text.
- 05What is at risk if wrong?
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Key findings from the stored brief
- Existing proactive paradigms assume query information is complete, leaving models only the wait or answer actions.
- AskStream contains 438 videos with 2,714 manually annotated frame-level time windows across wait, ask, and answer types.
- The strongest model attains only 52.18% on the Ask-F1 metric across both evaluation settings.
- The authors report that missing information can cause models to wait indefinitely or generate hallucinatory responses.
Source claims not independently checked; the benchmark's annotation process and Ask-F1 definition are described only at a high level in the supplied text.
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Why this may matter
Streaming video assistants must decide when to respond, and a model that cannot request missing context may stall or fabricate answers. A benchmark that measures when and what to ask gives the field a concrete way to track progress on this capability.
Limits and open questions
Source claims not independently checked; the benchmark's annotation process and Ask-F1 definition are described only at a high level in the supplied text.
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