Seeing Anomalies in the Dark: Video Anomaly Understanding with Low-Light Conditions for Multimodal LLMs

Video anomaly understanding (VAU) aims to detect and classify anomalous events in videos, providing fine-grained explanations of their nature and context. However, existing works in this direction often assume well-lit scenes, paying limited attention to low-light scenarios where many real-world anomalies occur, e.g., nighttime intrusions and blackout looting. To address this challenge, we introduce VAULT, a benchmark for low-light video anomaly understanding across various granularities. Specifically, VAULT contributes: (1) broad coverage of poorly illuminated anomalies with four illumination levels, covering 25 anomaly categories across 6 scene types, and (2) a unified evaluation suite comprising four tasks, i.e., temporal grounding, anomaly classification, anomaly description, and video question answering (VQA). Building upon VAULT, we propose EventVAULT, an event-guided framework that combines RGB appearance cues with event-stream dynamics for low-light VAU. Instead of relying solely on RGB appearance, EventVAULT leverages event streams to capture transient dynamics and address three key challenges: when to acquire informative RGB evidence, where to focus within the scene, and how anomalous events evolve. Extensive experiments demonstrate that the performance of existing MLLMs deteriorates as illumination decreases, while EventVAULT consistently improves performance across all four tasks. Our dataset and findings jointly highlight the need for more robust VAU under extreme visual conditions. Code available at: https://anonymous.4open.science/r/VAULT.