StreamVibe: Proactive Video Agents for Evolving User Intent
StreamVibe introduces StreamVibe-Bench, a benchmark with 4.1K evaluation and 85K training samples covering explicit, implicit, and dynamic user intents. It also presents StreamVibe-VL, a streaming proactive model that infers implicit intents from continuous observations and adapts as they evolve, and StreamVibe-Harness, which keeps intents as persistent states via hierarchical memory. The authors report a 10.7% improvement in response timing and answer quality over baselines and a 12.3% gain in proactive interaction performance.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
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StreamVibe introduces StreamVibe-Bench, a benchmark with 4.1K evaluation and 85K training samples covering explicit, implicit, and dynamic user intents. It also presents StreamVibe-VL, a streaming proactive model that infers implicit intents from continuous observations and adapts as they evolve, and StreamVibe-Harness, which keeps intents as persistent states via hierarchical memory. The authors report a 10.7% improvement in response timing and answer quality over baselines and a 12.3% gain in proactive interaction performance.
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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; reported gains are relative to the authors' chosen baselines.
- 05What is at risk if wrong?
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- 06What would change the view?
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Key findings from the stored brief
- StreamVibe-VL reportedly improves response timing and answer quality by 10.7% over baselines while preserving general video understanding.
- Existing proactive models are described as limited in understanding user intent and deciding when to respond, especially for implicit and dynamic intents.
- StreamVibe-Harness maintains user intents as persistent states with flexible lifecycles via hierarchical memory, reportedly yielding a 12.3% gain in proactive interaction.
Source claims not independently checked; reported gains are relative to the authors' chosen baselines.
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Evidence & sources
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Why this may matter
Proactive video agents that infer unstated user goals could make assistants more useful during live tasks such as website navigation. The benchmark and memory-based intent tracking offer a way to measure and improve this capability.
Limits and open questions
Source claims not independently checked; reported gains are relative to the authors' chosen baselines.
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