title: VAU-R1: Advancing Video Anomaly Understanding via Reinforcement
    Fine-Tuning
summary: Video Anomaly Understanding (VAU) is essential for applications such as smart
  cities, security surveillance, and disaster alert systems, yet remains
  challenging due to its demand for fine-grained spatio-temporal perception and
  robust reasoning under ambiguity. Despite advances in anomaly detection,
  existing methods often lack interpretability and struggle to capture the causal
  and contextual aspects of abnormal events. This limitation is further
  compounded by the absence of comprehensive benchmarks for evaluating reasoning
  ability in anomaly scenarios. To address both challenges, we introduce VAU-R1,
  a data-efficient framework built upon Multimodal Large Language Models (MLLMs),
  which enhances anomaly reasoning through Reinforcement Fine-Tuning (RFT).
  Besides, we propose VAU-Bench, the first Chain-of-Thought benchmark tailored
  for video anomaly reasoning, featuring multiple-choice QA, detailed rationales,
  temporal annotations, and descriptive captions. Empirical results show that
  VAU-R1 significantly improves question answering accuracy, temporal grounding,
  and reasoning coherence across diverse contexts. Together, our method and
  benchmark establish a strong foundation for interpretable and reasoning-aware
  video anomaly understanding. Our code is available at
  https://github.com/GVCLab/VAU-R1.
paper_authors._id: 683e501c89dc42ba0515a4d8
paper_authors.name: Liyun Zhu
paper_authors._id: 683e501c89dc42ba0515a4d9
paper_authors.name: Qixiang Chen
paper_authors._id: 683e501c89dc42ba0515a4da
paper_authors.name: Xi Shen
paper_authors._id: 683e501c89dc42ba0515a4db
paper_authors.name: Xiaodong Cun
paper_title: VAU-R1: Advancing Video Anomaly Understanding via Reinforcement
    Fine-Tuning
paper_summary: Video Anomaly Understanding (VAU) is essential for applications such as smart
  cities, security surveillance, and disaster alert systems, yet remains
  challenging due to its demand for fine-grained spatio-temporal perception and
  robust reasoning under ambiguity. Despite advances in anomaly detection,
  existing methods often lack interpretability and struggle to capture the causal
  and contextual aspects of abnormal events. This limitation is further
  compounded by the absence of comprehensive benchmarks for evaluating reasoning
  ability in anomaly scenarios. To address both challenges, we introduce VAU-R1,
  a data-efficient framework built upon Multimodal Large Language Models (MLLMs),
  which enhances anomaly reasoning through Reinforcement Fine-Tuning (RFT).
  Besides, we propose VAU-Bench, the first Chain-of-Thought benchmark tailored
  for video anomaly reasoning, featuring multiple-choice QA, detailed rationales,
  temporal annotations, and descriptive captions. Empirical results show that
  VAU-R1 significantly improves question answering accuracy, temporal grounding,
  and reasoning coherence across diverse contexts. Together, our method and
  benchmark establish a strong foundation for interpretable and reasoning-aware
  video anomaly understanding. Our code is available at
  https://github.com/GVCLab/VAU-R1.
paper_ai_summary: VAU-R1 uses Multimodal Large Language Models with Reinforcement Fine-Tuning to enhance video anomaly reasoning, complemented by VAU-Bench, a Chain-of-Thought benchmark for evaluating anomaly understanding.
paper_ai_keywords: Multimodal Large Language Models (MLLMs) Reinforcement Fine-Tuning (RFT) Chain-of-Thought benchmark question answering temporal grounding reasoning coherence