StreamVibe: Proactive Video Agents for Evolving User Intent
StreamVibe: Proactive Video Agents for Evolving User Intent
StreamVibe-Bench releases a benchmark containing 4.1K evaluation samples and 85K training samples for testing explicit, implicit, and dynamic user intent interaction; StreamVibe-VL is a streaming active video language model that infers implicit intentions and dynamically adjusts interaction by continuously observing; StreamVibe-Harness maintains user intentions as persistent states through a hierarchical memory framework, tracking the latest user intentions and environmental states, improving the consistency of proactive interaction responses by 12.3%.
Published on Oct 26
Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
StreamVibe introduces a benchmark, streaming vision-language model, and hierarchical memory harness to enable proactive, intent-aware interaction with implicit and evolving user goals.
Vision-language models are shifting from offline passive understanding toward online proactive interaction. However, prevailing methods largely rely on explicit requests while miss the implicit user intent (e.g., "where should I click next?" while a user navigate a website), meanwhile fail to update the state simultaneously as user requests might update continuously. These limitations make them less suited to intent-aware interaction in practical. To address these challenges, we introduce StreamVibe-Bench, a new benchmark serving as testbed for user intent interaction, with 4.1K evaluation samples and 85K training samples spanning explicit, implicit, and dynamic intents. Building on this benchmark, we develop StreamVibe-VL, a streaming proactive model that infers implicit user intents from continuous observations and dynamically tailors its interactions as these intents evolve. We further introduce StreamVibe-Harness, which maintains user intents as persistent states with a fleixble lifecycles via a hierarchical memory to track the latest user intent and environmental state. Our experiments reveal three key findings: (i) StreamVibe-VL preserves strong general video understanding while improving response timing and answer quality by 10.7% over other baselines; (ii) existing proactive models remain limited in understanding user intent and deciding when to respond, especially under implicit and dynamic intents; and (iii) StreamVibe-Harness further improves response consistency and adaptation over sustained interaction, resulting in a 12.3% gain in proactive interaction.
View arXiv page View PDF Add to collection
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2610.34662 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2610.34662 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2610.34662 in a Space README.md to link it from this page.
Collections including this paper 1
Source: Hugging Face Daily Papers · huggingface.co