AURA: Always-On Understanding and Real-Time Assistance via Video Streams

• URL: http://arxiv.org/abs/2604.04184v1
• Date: Sun, 05 Apr 2026 16:53:46 GMT
• Title: AURA: Always-On Understanding and Real-Time Assistance via Video Streams
• Abstract summary: AURA (Always-On Understanding and Real-Time Assistance) is an end-to-end streaming visual interaction framework.<n>It enables a unified VideoLLM to continuously process video streams and support both real-time question answering and proactive responses.<n>AURA achieves state-of-the-art performance on streaming benchmarks and supports a real-time demo system with ASR and TTS running at 2 FPS on two 80G accelerators.
• Score: 37.80002196035961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Large Language Models (VideoLLMs) have achieved strong performance on many video understanding tasks, but most existing systems remain offline and are not well-suited for live video streams that require continuous observation and timely response. Recent streaming VideoLLMs have made progress, yet current approaches often rely on decoupled trigger-response pipelines or are limited to captioning-style narration, reducing their effectiveness for open-ended question answering and long-horizon interaction. We propose AURA (Always-On Understanding and Real-Time Assistance), an end-to-end streaming visual interaction framework that enables a unified VideoLLM to continuously process video streams and support both real-time question answering and proactive responses. AURA integrates context management, data construction, training objectives, and deployment optimization for stable long-horizon streaming interaction. It achieves state-of-the-art performance on streaming benchmarks and supports a real-time demo system with ASR and TTS running at 2 FPS on two 80G accelerators. We release the AURA model together with a real-time inference framework to facilitate future research.

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