Separate to Deliberate, Fuse to Decide: Perspective-Factorized Latent Reasoning for Video Anomaly Understanding

Video Anomaly Understanding (VAU) requires models not only to recognize unusual visual content, but also to understand how scene states change and how events evolve over time. These complementary perspectives provide distinct yet mutually informative cues for interpreting complex anomalies. However, existing LLM-based VAU methods typically encode heterogeneous visual information into a shared representation stream or perform explicit reasoning through a single textual chain of thought, which may prematurely entangle information from complementary perspectives. To address this limitation, we propose BRAID, a perspective-factorized latent reasoning framework based on an observe–deliberate–synthesize paradigm. Specifically, for perspective-aligned observation, BRAID captures object structures, spatial configurations, and state changes in the scene, and models temporal interactions and event evolution. It then performs perspective-specific latent deliberation over these observations, allowing each reasoning stream to develop from the visual evidence captured under its corresponding perspective while avoiding premature cross-perspective interference. Finally, since the relevance of different perspectives varies across contexts, BRAID employs a contextualized latent synthesis mechanism to dynamically assess and integrate perspective-specific information, enabling the model to jointly leverage complementary information from different perspectives for anomaly understanding and response generation. Extensive experiments on multiple widely used benchmarks demonstrate that our method outperforms existing MLLM-based VAU methods in both accuracy and computational efficiency.