StreamVibe: Streaming proactive video agents lift implicit-intent interaction by 12.3%
_akhaliq · x · 2026-10-27
A new paper, StreamVibe, targets the gap as vision-language models move from offline passive understanding to online proactive interaction: current methods rely on explicit requests, miss implicit user intents (e.g., "where should I click next?" while a user browses), and fail to keep updating as intents evolve.
Key contributions:
- StreamVibe-Bench: a benchmark spanning explicit, implicit, and dynamic intents, with 4.1K evaluation and 85K training samples
- StreamVibe-VL: a streaming proactive model that infers implicit intents from continuous observations and adapts as they evolve, improving response timing and answer quality by 10.7% over baselines while retaining strong general video understanding
- StreamVibe-Harness: maintains user intents as persistent states with flexible lifecycles via hierarchical memory, adding a further 12.3% gain in proactive interaction performance
The experiments also show existing proactive models remain limited at understanding intents and deciding when to respond, especially under implicit and dynamic intents.
Related event: Google and Partners Unveil StreamVibe, a Streaming Video Agent(2 posts)→
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