The field of multimodal video understanding is rapidly advancing, with a focus on developing innovative models and frameworks that can efficiently process and analyze long videos. Recent research has highlighted the importance of multimodal coreference resolution, online filtering, and large multimodal models for video understanding and editing. Notably, the development of new datasets and benchmark approaches is facilitating progress in this area. Some notable papers have made significant contributions to the field. The papers include: Multimodal Coreference Resolution for Chinese Social Media Dialogues, which introduces a new dataset and benchmark approach for multimodal coreference resolution. ReSpec, which proposes an online filtering framework for learning on video-text data streams. Vidi, which introduces a family of large multimodal models for video understanding and editing. ViSMaP, which presents an unsupervised video summarization system using meta-prompting. MR. Video, which proposes a MapReduce principle for long video understanding. MCAF, which introduces a multimodal coarse-to-fine attention focusing framework for video understanding. FRAG, which proposes a frame selection augmented generation framework for long video and long document understanding.