The field of multimodal time series understanding and video language models is rapidly advancing, with a focus on developing benchmarks and evaluation methods to assess the abilities of large language models (LLMs) in understanding complex temporal relationships and multimodal data. Recent research has highlighted the importance of temporal information in time series classification and the need for more robust evaluation frameworks. Notably, the development of new benchmarks such as MTBench and 4D-Bench has enabled the evaluation of LLMs on tasks that require joint reasoning over structured numerical trends and unstructured textual narratives, as well as 4D object understanding. Meanwhile, video language models are being pushed to new limits with the introduction of benchmarks like Video SimpleQA and RoadSocial, which focus on factuality evaluation and road event understanding from social video narratives. These advancements demonstrate the growing interest in developing more sophisticated models that can effectively integrate multimodal information and reason about complex temporal relationships. Some noteworthy papers include: MTBench, which introduces a large-scale benchmark for evaluating LLMs on time series and text understanding across financial and weather domains. 4D-Bench, which provides the first benchmark for evaluating the capabilities of multimodal large language models in 4D object understanding. Video SimpleQA, which introduces a comprehensive benchmark tailored for factuality evaluation of large video language models.