title: StreamBridge: Turning Your Offline Video Large Language Model into a
    Proactive Streaming Assistant
summary: We present StreamBridge, a simple yet effective framework that seamlessly
  transforms offline Video-LLMs into streaming-capable models. It addresses two
  fundamental challenges in adapting existing models into online scenarios: (1)
  limited capability for multi-turn real-time understanding, and (2) lack of
  proactive response mechanisms. Specifically, StreamBridge incorporates (1) a
  memory buffer combined with a round-decayed compression strategy, supporting
  long-context multi-turn interactions, and (2) a decoupled, lightweight
  activation model that can be effortlessly integrated into existing Video-LLMs,
  enabling continuous proactive responses. To further support StreamBridge, we
  construct Stream-IT, a large-scale dataset tailored for streaming video
  understanding, featuring interleaved video-text sequences and diverse
  instruction formats. Extensive experiments show that StreamBridge significantly
  improves the streaming understanding capabilities of offline Video-LLMs across
  various tasks, outperforming even proprietary models such as GPT-4o and Gemini
  1.5 Pro. Simultaneously, it achieves competitive or superior performance on
  standard video understanding benchmarks.
paper_authors._id: 681d95b5c7ae5f65b0e55ff9
paper_authors.name: Haibo Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T07:21:20.829Z
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paper_authors.avatarUrl: /avatars/c1df1899e3925aa6fdfc8ee0049fa8a7.svg
paper_authors.fullname: WANG HAIBO
paper_authors.type: user
paper_authors.user: WHB139426
paper_authors._id: 681d95b5c7ae5f65b0e55ffa
paper_authors.name: Bo Feng
paper_authors._id: 681d95b5c7ae5f65b0e55ffb
paper_authors.name: Zhengfeng Lai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T09:00:45.264Z
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paper_authors.avatarUrl: /avatars/a35568fb933ceef7451bd88fb3d5ab17.svg
paper_authors.fullname: Zhengfeng Lai
paper_authors.type: user
paper_authors.user: jefflai
paper_authors._id: 681d95b5c7ae5f65b0e55ffc
paper_authors.name: Mingze Xu
paper_authors._id: 681d95b5c7ae5f65b0e55ffd
paper_authors.name: Shiyu Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T09:01:08.366Z
paper_authors._id: 67fa856547b40f55b7ff3ce5
paper_authors.avatarUrl: /avatars/745937497772e9b533ba7940d758d30d.svg
paper_authors.fullname: Shiyu Li
paper_authors.type: user
paper_authors.user: ShiyuLi
paper_authors._id: 681d95b5c7ae5f65b0e55ffe
paper_authors.name: Weifeng Ge
paper_authors._id: 681d95b5c7ae5f65b0e55fff
paper_authors.name: Afshin Dehghan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T09:01:21.716Z
paper_authors._id: 66fc2377516eaf950d4b8209
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/mcUxUxXy18Gv9KvCW23s0.png
paper_authors.fullname: Afshin Dehghan
paper_authors.type: user
paper_authors.user: afshindn
paper_authors._id: 681d95b5c7ae5f65b0e56000
paper_authors.name: Meng Cao
paper_authors._id: 681d95b5c7ae5f65b0e56001
paper_authors.name: Ping Huang
paper_title: StreamBridge: Turning Your Offline Video Large Language Model into a
    Proactive Streaming Assistant
paper_summary: We present StreamBridge, a simple yet effective framework that seamlessly
  transforms offline Video-LLMs into streaming-capable models. It addresses two
  fundamental challenges in adapting existing models into online scenarios: (1)
  limited capability for multi-turn real-time understanding, and (2) lack of
  proactive response mechanisms. Specifically, StreamBridge incorporates (1) a
  memory buffer combined with a round-decayed compression strategy, supporting
  long-context multi-turn interactions, and (2) a decoupled, lightweight
  activation model that can be effortlessly integrated into existing Video-LLMs,
  enabling continuous proactive responses. To further support StreamBridge, we
  construct Stream-IT, a large-scale dataset tailored for streaming video
  understanding, featuring interleaved video-text sequences and diverse
  instruction formats. Extensive experiments show that StreamBridge significantly
  improves the streaming understanding capabilities of offline Video-LLMs across
  various tasks, outperforming even proprietary models such as GPT-4o and Gemini
  1.5 Pro. Simultaneously, it achieves competitive or superior performance on
  standard video understanding benchmarks.
paper_ai_summary: StreamBridge is a framework that enhances offline Video-LLMs for streaming capabilities through a memory buffer with compression and a proactive response model, demonstrating superior performance on video understanding tasks.
paper_ai_keywords: Video-LLMs streaming-capable models multi-turn real-time understanding round-decayed compression strategy proactive response mechanisms interleaved video-text sequences Stream-IT dataset