ProactiveBench: Can Streaming Video Models Really Interact Like Humans?

2026-09-11

AI Summary

本文通过引入ProactiveBench评估流媒体视频模型在没有明确响应提示下，以一秒为间隔进行主动交互的能力，揭示了现有系统在类人互动所需的时间决策上的不足。

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 ProactiveBench, 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 four of the six evaluated systems, revealing a substantial gap in the temporal decision-making required for human-like interaction.

Problem

• Streaming video understanding
• Proactive interaction
• Temporal context
• Human-like interaction

Innovation

• Proactive Interaction
• Streaming Video Understanding
• Temporal Decision-Making
• ProactiveBench