The field of generative modeling and neural networks is rapidly evolving, with a focus on developing more efficient and effective methods for modeling complex data distributions. Recent research has explored the use of flow-based models, hypergraph structure learning, and geometric flow models to improve the accuracy and flexibility of generative models. Additionally, there is a growing interest in incorporating symmetries and geometry into neural network architectures to reduce dimensionality and improve generalization. Noteworthy papers in this area include the development of a flow-based method for learning all-to-all transfer maps among conditional distributions, which enables simultaneous learning of optimal transports for all pairs of conditional distributions. Another notable work proposes a novel hypergraph learning method that recovers a hypergraph topology from time-series signals based on a smoothness prior, demonstrating improved performance over other state-of-the-art hypergraph inference methods. Furthermore, research on geometric flow models over neural network weights has shown that modeling the geometry of neural networks more faithfully leads to more effective flow models that can generalize to different tasks and architectures. The development of new methods and techniques in this area has the potential to impact a wide range of applications, from image and video generation to natural language processing and reinforcement learning.