AI BriefingStreamVibe-VL Improves Proactive Video Interaction by 10.7% Over Baselines
·2026.10.28 09:00
Key point
The system uses a hierarchical memory harness to track evolving user intent, yielding a further 12.3% gain in sustained interaction consistency.
Details
- StreamVibe-VL is a streaming proactive model that infers implicit user intents from continuous observations, improving response timing and answer quality by 10.7% over other baselines.
- StreamVibe-Harness maintains user intents as persistent states with flexible lifecycles via hierarchical memory, resulting in a 12.3% gain in proactive interaction consistency and adaptation.
- StreamVibe-Bench is a new benchmark with 4.1K evaluation samples and 85K training samples spanning explicit, implicit, and dynamic intents.
- Existing proactive models remain limited in understanding user intent and deciding when to respond, particularly under implicit and dynamic intents.
- The approach addresses the shift of vision-language models from offline passive understanding to online proactive interaction.
- StreamVibe-VL preserves strong general video understanding capabilities while enhancing proactive response metrics.
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