The recent advancements in the research area predominantly revolve around the integration of state space models (SSMs) with various domains, particularly in addressing computational inefficiencies and enhancing performance in tasks such as image deblurring, biodiversity analysis, dynamic graph embedding, and text reranking. SSMs, exemplified by architectures like Mamba, are being leveraged to replace traditional transformer-based models due to their linear complexity and ability to handle long-context data more efficiently. This shift is evident in applications ranging from visual data processing to temporal graph modeling, where SSMs are shown to achieve comparable or superior performance while reducing computational overhead. Additionally, there is a growing focus on fairness in machine learning models, particularly in graph neural networks and transformers, where novel frameworks are being developed to mitigate biases without reliance on sensitive attributes. These developments indicate a trend towards more efficient, fair, and scalable solutions across diverse fields, with notable innovations in model architecture and learning paradigms.