StreamVibe: a benchmark and streaming video-language model for inferring users' implicit and changing intent from live screens
The paper introduces StreamVibe-Bench, a benchmark with 4.1K evaluation samples and 85K training samples spanning 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 intents 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 from the harness.
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- Primary sourceHugging Face discovery · source check open
- Discovery linkHugging Face
- Linked in sourceView PDF
- Linked in sourcearXiv:2610.34662v1
- Linked in sourceLearn more about arXivLabs
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The paper introduces StreamVibe-Bench, a benchmark with 4.1K evaluation samples and 85K training samples spanning 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 intents 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 from the harness.
Key findings from the stored brief
- StreamVibe-VL reportedly improves response timing and answer quality by 10.7% over other 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 using hierarchical memory, reportedly yielding a 12.3% gain in proactive interaction.
Source claims not independently checked; reported gains are the authors' own results on their own benchmark.
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Why this may matter
Most video-language models answer explicit requests offline. Benchmarks and models that infer implicit, changing intent from live screens could enable more useful proactive assistants for tasks like guiding users through websites.
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Source claims not independently checked; reported gains are the authors' own results on their own benchmark.
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