The field of time series analytics and multimodal learning is rapidly evolving, with a focus on developing innovative architectures and frameworks that can effectively handle complex, dynamic data. Recent developments have centered around improving the adaptability and interpretability of models, particularly in applications where multiple modalities and variables are involved. Notably, Mixture-of-Experts (MoE) architectures have emerged as a powerful tool for task-aware time series analytics, allowing for more efficient and specialized processing of diverse data types. Furthermore, advances in multimodal learning have enabled the integration of multiple data sources and modalities, enhancing the accuracy and robustness of models in real-world applications. The incorporation of techniques such as sparse attention, adaptive routing, and modality-aware design has also contributed to significant improvements in model performance and efficiency. Overall, these developments are driving progress in various fields, including healthcare, finance, and IoT applications, where accurate and reliable time series analytics and multimodal learning are crucial. Some noteworthy papers in this area include: Unlocking the Power of Mixture-of-Experts for Task-Aware Time Series Analytics, which proposes a novel MoE-based framework for time series analytics. MAESTRO: Adaptive Sparse Attention and Robust Learning for Multimodal Dynamic Time Series, which introduces a framework that overcomes key limitations of existing multimodal learning approaches. Guiding Mixture-of-Experts with Temporal Multimodal Interactions, which proposes a novel framework that guides MoE routing using quantified temporal interaction. UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression, which proposes a unified framework for multi-modal and multi-class anomaly detection. Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection, which proposes a supervised modality conversion-based multivariate time series anomaly detection framework.