The fields of quality of experience (QoE), language modeling, and time series forecasting are witnessing significant advancements, driven by the adoption of machine learning, tensor factorization, and innovative modeling techniques. Researchers are exploring alternative methods to traditional quality of service (QoS) metrics, focusing on dynamic and real-time predictions. Notable papers, such as Generative QoE Modeling and D-Tracker, introduce lightweight approaches and methods for capturing temporal patterns and forecasting future activities. In language modeling, researchers are developing novel architectures and techniques to enhance the expressivity and power of large language models, while reducing computational requirements. Papers like PARD, MCD-TSF, and WuNeng demonstrate promising results in accelerating inference, improving performance, and enhancing expressivity. The field of environmental and weather forecasting is also rapidly advancing, with the development of new deep learning models and techniques, such as the SSA-UNet and UNet with Axial Transformer, which have achieved state-of-the-art results in several benchmark datasets. Furthermore, researchers are exploring new methods for combating dimensional collapse in pre-training data, such as diversified file selection algorithms, to enhance diversity and improve overall performance. Overall, these advances have the potential to significantly impact various fields, including content marketing, customer service chatbots, and legal document drafting, and demonstrate the growing importance of predictive modeling and language processing in driving innovation and improvement in numerous applications.