Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-F

arXiv:2510.20579v2 Announce Type: replace-cross Abstract: Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence

arXiv:2608.07959v1 Announce Type: new Abstract: Ultra-long egocentric video understanding requires reasoning over temporally sparse evidence distributed across hours or days,

arXiv:2607.18325v1 Announce Type: cross Abstract: Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining