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Streaming video LLMs must continuously capture temporal evidence before its downstream relevance is known and proactively respond when sufficient evidence arrives. Balancing real-time perception with reusable factual memory has remained a core challenge: revisiting raw visual features explodes memory, while lossy feature pooling destroys critical spatiotemporal context. Researchers from Nanjing University and Shanghai AI Lab introduce OneStreamer (arXiv:2610.01762, ~200 upvotes on Hugging Face). The framework introduces Proactive Hierarchical Caption Memory (PHCM) to generate time-grounded captions and event summaries on the fly, paired with Proactive State Transition Learning (PSTL) to prevent idle waiting frames from overwhelming optimization gradients. Built on the newly released OneStreamer-1M dataset, their 4B model establishes state-of-the-art results across all eight streaming video benchmarks.

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Streaming video LLMs must preserve perceptual evidence before downstream intent is revealed and respond proactively when evidence matures. Nanjing University researchers introduce OneStreamer, a unified architecture that couples query-independent factual recording with task execution through a shared proactive generation interface. Its Proactive Hierarchical Caption Memory (PHCM) synthesizes time-anchored micro-captions and event summaries, allowing inference to complement recent visual frames with cached factual records without reprocessing raw historical visual features. Trained on the broad-coverage OneStreamer-1M dataset, the 4B model establishes top SOTA across all eight streaming video understanding benchmarks.

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