NEXUS: Neuro-Symbolic Event Reasoning for Training-Free Video Anomaly Detection

Training-free video anomaly detection is attractive for real-world surveillance, where collecting representative training data is often constrained by privacy, data-governance requirements, and the inherent rarity of abnormal events. However, existing training-free approaches remain largely driven by visual affinity, either through direct vision-language similarity or by prompting multi-modal models to infer anomaly decisions from appearance-level evidence. We argue that this view is incomplete: real-world complex anomalies are not only isolated appearances, but also temporally evolving events whose interpretation depends on how semantic states emerge, persist, change, and resolve over time. To this end, we introduce Neuro-Symbolic Event eXploration with Unified Spatio-temporal VLLM Verification (NEXUS), a novel training-free framework for video anomaly detection. At its core, NEXUS introduces Neuro-symbolic Temporal Event Reasoning (N-TER), which transforms frozen vision-language evidence into five continuous event states and composes them through interpretable temporal chains capturing event onset, reversal, persistence, consequence, resolution, and prior history. Complementing this temporal reasoning, Uncertainty-guided Proposal Scoring (UPS) explicitly enforces an anomaly hypothesis to outperform both uncertain and normal alternatives, preventing visual similarity alone from becoming anomaly evidence. Then, Reasoning-Enabled Score Refinement (RESR) couples UPS proposals with N-TER consistency and contradiction, allowing temporal reasoning to refine visual hypotheses and also minimizing anomaly hallucination. A frozen VLLM Judge then performs the final spatio-temporal verification with the symbolic evidence. We evaluate NEXUS across five real-world challenging video anomaly detection benchmarks and demonstrate strong performance gain against state-of-the-art methods.