StreamVibe: Proactive Video Agents for Evolving User Intent
In brief
- StreamVibe-Bench provides 4,100 tests and 85,000 training samples to assess explicit, implicit, and dynamic user intents.
- StreamVibe-VL infers implicit intents in real time and adapts responses, improving quality by 10.7%.
- StreamVibe-Harness boosts consistency in long interactions with a 12.3% gain.
Vision-language models are moving from offline passive understanding to real-time proactive interaction. Current methods mostly rely on explicit user requests and miss implicit intentions, such as evolving needs while browsing a website. According to the announcement, these gaps limit their effectiveness for intent-aware interactions.
To address this, researchers introduce StreamVibe-Bench, a new benchmark with 4,100 evaluation samples and 85,000 training samples covering explicit, implicit, and dynamic intents. They pair it with StreamVibe-VL, a streaming proactive model that infers implicit intents from continuous observations and adapts responses in real time. Tests show a 10.7% improvement in response quality over baselines, while StreamVibe-Harness enhances consistency over sustained interactions, delivering a 12.3% gain.
This summary gives you the essentials. The full article, its figures and quotes are on Hugging Face Daily Papers.
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