LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams
arXiv:2606.17798v1 Announce Type: cross Abstract: Despite the remarkable progress of Video Large Language Models (Video-LLMs), current online architectures still struggle to simultaneously process continuous video streams, decide autonomously when to respond, and preserve long-horizon contextual memory. These obstacles undermine real-time responsiveness and cause severe forgetting throughout prolonged interactions. In this work, we introduce LiveStarPro, a live streaming assistant that is designed for proactive video understanding over long-horizon streams. The design of LiveStarPro rests on t
The rapid evolution of Video Large Language Models (Video-LLMs) confronts limitations in real-time continuous processing and long-horizon memory, creating a timely need for more robust streaming understanding systems.
This development addressing real-time responsiveness and contextual memory in video understanding can significantly advance the capability and autonomy of AI systems interacting with dynamic visual environments.
AI systems can now process continuous video streams more effectively, make proactive decisions, and retain context over extended periods, moving beyond the limitations of current online architectures.
- · AI developers
- · Surveillance and security sector
- · Autonomous systems developers
- · Live streaming platforms
- · AI systems with poor memory architectures
- · Manual video monitoring services
Improved performance and reliability of AI applications requiring real-time video analysis.
Expansion of autonomous agents into more complex and dynamic environments that require continuous visual understanding.
Enhanced AI-driven proactive decision-making in critical applications, potentially reducing human intervention in dynamic scenarios.
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Read at arXiv cs.AI