The field of video understanding and reasoning is rapidly advancing, with a focus on developing models that can effectively interpret and analyze video content. Recent developments have highlighted the importance of fine-grained reasoning, temporal understanding, and multimodal fusion in achieving accurate video understanding. Notably, researchers are exploring new benchmarks and frameworks that evaluate models' ability to reason about human speech, object interactions, and task-oriented grounding in videos. Furthermore, there is a growing interest in developing models that can anticipate actions, detect mistakes, and understand procedural activities in instructional videos. Overall, the field is moving towards more comprehensive and nuanced video understanding, with potential applications in areas such as embodied intelligence, assistive AI, and human-computer interaction. Some noteworthy papers in this regard include HanDyVQA, which introduces a fine-grained video question-answering benchmark for hand-object interaction dynamics, and StreamGaze, which evaluates models' ability to use gaze signals for temporal and proactive reasoning in streaming videos. Additionally, papers like ToG-Bench and StreamEQA are pushing the boundaries of task-oriented spatio-temporal grounding and streaming video understanding, respectively.