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arXiv · 2609.12658

LiveProBench: Can Streaming Video Models Really Interact Like Humans?

Kaixuan Du·Xin Wan·Hang Zhang·Meng Cao·Dai Guan·Ming Chen·YuKun Wang
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Abstract

Streaming video understanding requires models to process continuous multimodal input while maintaining temporal context. Existing evaluations are predominantly reactive: they query a model at a selected timestamp and therefore do not assess when it should respond. Proactive interaction instead requires monitoring a standing request, responding within an appropriate interval after the target event, and otherwise remaining silent. We introduce LiveProBench, which evaluates models at one-second stream intervals without an explicit response cue. Its six subtasks vary trigger ambiguity and timing tolerance. Event Sensitivity geometrically combines response and silence rates on the same recording; four window-based subtasks distinguish early, in-window, and missed responses; and Duplicate Counting penalizes omissions and repetitions. Premature responses outnumber missed responses for half of the evaluated models, revealing a substantial gap in the temporal decision-making required for human-like interaction.

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Kaixuan Du, Xin Wan, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, YuKun Wang. 2026-09-22. LiveProBench: Can Streaming Video Models Really Interact Like Humans?. https://arxiv.org/abs/2609.12658

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