The field of video understanding is moving towards more dynamic and interactive approaches, with a focus on incorporating multimodal data and improving temporal reasoning. Recent developments have highlighted the importance of adaptive sampling and keyframe selection in enhancing computational efficiency while preserving temporal information. Additionally, the integration of language models and multimodal large language models has shown promise in improving group activity detection and real-time threat monitoring. Noteworthy papers include: Video2Roleplay, which introduces a multimodal dataset and framework for video-guided role-playing agents, and ChronoForge-RL, which proposes a novel video understanding framework combining Temporal Apex Distillation and KeyFrame-aware Group Relative Policy Optimization. Language-Instructed Reasoning for Group Activity Detection via Multimodal Large Language Model and Live-E2T also demonstrate innovative approaches to group activity detection and real-time threat monitoring. COLT enhances video large language models with continual tool usage, allowing for automatic acquisition of tool-use ability in a successive tool stream.