VLX-Flow: Continuous Video Understanding for Real-Time Multimodal Interaction

VLX-Flow is a novel model designed for real-time video understanding, addressing the limitations of traditional video models that wait for user queries before processing. Unlike offline workflows which require reprocessing entire video histories, VLX-Flow continuously processes video streams in chronological chunks, updating its internal memory incrementally. This allows it to answer questions based on a maintained state without rewatching the video, making it more efficient for live environments. The model uses a two-layer memory system, with a visual cache for short-term details and semantic memory for higher-level context, ensuring stable latency and smoother memory growth. This approach supports real-time video question answering and event-triggered interactions, making it suitable for edge devices where bandwidth, latency, and privacy are concerns. VLX-Flow transforms video understanding into a continuously running perception module, ideal for devices that need to process video as a live, ongoing context.