The field of multimodal understanding and reasoning is rapidly advancing, with a focus on improving the ability of models to comprehend and interpret complex multimedia data. Recent developments have seen the introduction of novel frameworks and techniques that enhance the temporal awareness and reasoning capabilities of multimodal large language models. These advancements have led to significant improvements in tasks such as video understanding, visual question answering, and audio question answering. Notably, the use of structured multi-video collaborative reasoning, error-aware curriculum learning, and stochastic clock attention mechanisms have shown promising results. Furthermore, the application of multimodal large language models to zero-shot spatio-temporal video grounding has demonstrated the potential for these models to effectively localize and understand video content without requiring extensive training data. Overall, the field is moving towards more sophisticated and effective methods for multimodal understanding and reasoning, with a focus on real-world applications and deployments. Noteworthy papers include: LaV-CoT, which achieves state-of-the-art performance in multilingual visual question answering, and Unleashing the Potential of Multimodal LLMs for Zero-Shot Spatio-Temporal Video Grounding, which proposes a novel framework for zero-shot video grounding.