arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalating latency and memory usage. Processing entire videos with full attention leads to quadratic computational costs and poor performance on long videos. Meanwhile, simple sliding window methods are also flawed, as they either break coherence or suffer from high latency due to redundant recomputation. In this paper, we introduce StreamingVLM, a model designed for
The development of StreamingVLM addresses the critical challenges of latency and memory in processing continuous, real-time video streams for AI models, a key limitation for current applications.
This breakthrough provides a pathway for truly real-time, coherent understanding in AI systems, enabling new applications in autonomous agents and AI assistants that require continuous visual processing.
Traditional VLM approaches are constrained by quadratic computational costs for long videos; StreamingVLM enables efficient and coherent processing of infinite video streams without escalating costs or latency.
- · AI agents developers
- · Robotics companies
- · Surveillance technology providers
- · Cloud computing providers
- · Legacy video processing solutions
- · VLMs highly dependent on batched, finite video processing
- · Data architectures not optimized for streaming inputs
AI models gain enhanced real-time awareness and context from continuous video inputs.
This improved real-time understanding accelerates the development and deployment of truly autonomous AI agents in various sectors.
Ubiquitous, always-on AI assistants capable of comprehending complex, dynamic environments become a realistic prospect, transforming human-computer interaction.
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Read at arXiv cs.CL