The field of neural networks is evolving rapidly, with a growing focus on developing more efficient and effective architectures. Recent research has explored the use of gradient descent as a shrinkage operator, allowing for more precise control over spectral bias in neural networks. Additionally, advancements in attention mechanisms have enabled the development of more powerful and flexible models, capable of approximating complex functions and capturing fine-scale geometric features. The integration of geometry-informed neural operators with transformer architectures has also shown significant promise, enabling accurate predictions for arbitrary geometries. Furthermore, novel frameworks for supervised pretraining and deep physics priors are being introduced, allowing for more accurate material property predictions and first-order inverse optimization. Noteworthy papers include: Gradient Descent as a Shrinkage Operator for Spectral Bias, which proposes an explicit relationship between gradient descent hyperparameters and bandwidth. Geometry-Informed Neural Operator Transformer, which introduces a novel architecture for forward predictions on arbitrary geometries. Attention Mechanism, Max-Affine Partition, and Universal Approximation, which establishes the universal approximation capability of single-layer attention mechanisms. Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modeling, which introduces a self-supervised method for capturing fine-scale geometric features.