Chengzhi Liu, Yuxiang Lai and co-authors (arXiv:2610.34662) · Proactive video agent

StreamVibe

StreamVibe is a project for proactive video assistants that must decide when to speak as a user's intent is implicit or changes during a task. It has three parts: StreamVibe-Bench, the StreamVibe-VL model and the StreamVibe-Harness. The benchmark has 4,100 evaluation samples (600 explicit, 2,000 implicit, 1,500 dynamic) with 20,546 annotated help points over 183.4 hours of physical and digital task video. Gemini-3.8-Flash proposes help points and a sample is kept only if four reviewers approve it. Each second the model sees only the past and chooses to stay silent or respond. The Harness tracks requests, task progress and past replies, and retrieves relevant history. StreamVibe-VL is trained with adaptive streaming SFT (70K + 768K samples) and then GRPO-based localised trajectory RL on 15K tasks, rewarding timing, content and consistency. The page says it is the best open-source model at every intent level against 11 proprietary, offline and streaming models, and that implicit and dynamic intents remain hard for all models; the page gives no exact scores. Licence, code and release details are not stated.