The field of neural architecture design and bioinformatics is rapidly evolving, with a growing focus on developing novel, high-performance architectures tailored to specific biological data modalities. Recent research has explored the use of Neural Architecture Search (NAS) and other methods to discover optimal architectures for biological foundation models, leveraging techniques such as genetic algorithms and grammar-based sequence alignment. Notable developments include the application of hypergraphs in geometric deep learning for 3D RNA inverse folding and the proposal of new frameworks for automated architecture discovery. These advancements have the potential to significantly improve the accuracy and efficiency of bioinformatics models. Noteworthy papers include BioArc, which introduces a novel framework for automated architecture discovery for biological foundation models, and HyperRNA, which proposes a generative model leveraging hypergraphs for RNA sequence design. Additionally, the Network of Theseus method enables the conversion of trained neural networks into entirely different target architectures while preserving performance, expanding the space of viable inference-time architectures.