ProactiveBench: Can Streaming Video Models Really Interact Like Humans?
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Computer Science > Machine Learning
Title:ProactiveBench: Can Streaming Video Models Really Interact Like Humans?
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.
| Comments: | Code and data is available at this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.12658 [cs.LG] |
| (or arXiv:2609.12658v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.12658
arXiv-issued DOI via DataCite (pending registration)
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