arXiv: cs.AI preprintsInternational6 November 2026
StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
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arXiv:2512.01707v4 Announce Type: replace-cross Abstract: Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intent for realistic applications such as Augmented Reality (AR) glasses. While prior streaming benchmarks evaluate temporal reasoning, none measure whether Multimodal Large Language Models (MLLMs) can interpret or leverage human gaze signals within a streaming setting. To fill this gap, we introduce StreamGaze, the first benchmark designed to evaluate how effectively MLLMs utilize gaze for temporal and proactive rea
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