Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding

• URL: http://arxiv.org/abs/2606.06991v1
• Date: Fri, 05 Jun 2026 07:29:20 GMT
• Title: Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding
• Abstract summary: We introduce a novel paradigm for online video understanding: Streaming Video-Language Synchrony (SVLS)<n>We present LyraV, a live streaming assistant built upon a hierarchical control framework with two core innovations.<n>First, the Frame-Driven Transition Controller (FDTC), makes high-level semantic decisions on when to continue speaking, start a new response, or stay silent.<n>Second, the Streaming Token Pacer (SToP), a plug-and-play lightweight predictive module, dynamically adapts the language generation rate to match the pace of the visual content.
• Score: 69.296913137409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online Video Large Language Models (Video-LLMs) have advanced toward seamless human-AI interaction through frame-by-frame processing and proactive responding. However, a critical challenge remains in streaming scenarios: existing models typically pause video perception while generating responses, breaking real-time video-language synchrony and causing stutters. To address this, we introduce a novel paradigm for online video understanding: Streaming Video-Language Synchrony (SVLS), and present LyraV, a live streaming assistant built upon a hierarchical control framework with two core innovations. First, the Frame-Driven Transition Controller (FDTC), a training-free verification-based finite-state machine, makes high-level semantic decisions on when to continue speaking, start a new response, or stay silent. Second, the Streaming Token Pacer (SToP), a plug-and-play lightweight predictive module, dynamically adapts the language generation rate to match the pace of the visual content. Concretely, LyraV performs \emph{per-frame incremental, sub-budget decoding}: within each frame interval it emits only a small chunk of tokens that fits the real-time budget, so perception is never blocked for a full sentence. Together, these components enable LyraV to seamlessly interleave incoming video frames with generated word tokens, achieving a fine-grained synchrony. Extensive experiments conducted on five online and three offline benchmarks demonstrate that LyraV preserves the backbone's general understanding ability while substantially improving streaming synchrony and narrative fluency, delivering a 98.29\% synchrony with video playback and a real-time processing speed of 3.89 FPS. Interestingly, we observe an empirical capability in LyraV: dynamic reasoning over streaming tokens, enabling continuous interpretation and "thinking" alongside visual input.

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