The field of multimodal understanding and representation is witnessing significant advancements, driven by the development of innovative models and techniques that can effectively capture and process complex relationships between different modalities, such as vision, audio, and language. A key direction in this area is the focus on improving the efficiency and effectiveness of multimodal interaction modeling, with a particular emphasis on dense event localization, audio-visual segmentation, and 3D visual grounding. Notable papers in this area include LLaVA-Scissor, which proposes a training-free token compression strategy for video multimodal large language models, and DEL, which introduces a framework for dense semantic action localization in long untrimmed videos. Additionally, papers like ASDA and MUG have made significant contributions to self-supervised representation learning and audio-visual video parsing, respectively. Overall, these developments are pushing the boundaries of multimodal understanding and representation, enabling more accurate and efficient processing of complex multimedia data.