The fields of neural architecture design, bioinformatics, image restoration, and molecular generation are experiencing rapid growth, driven by advancements in techniques such as Neural Architecture Search (NAS), diffusion-based models, and generative models. A common theme among these fields is the development of novel, high-performance architectures and methods tailored to specific data modalities. In neural architecture design and bioinformatics, researchers have explored the application of hypergraphs in geometric deep learning for 3D RNA inverse folding and proposed new frameworks for automated architecture discovery. Notable papers include BioArc, which introduces a novel framework for automated architecture discovery, and HyperRNA, which proposes a generative model leveraging hypergraphs for RNA sequence design. The field of image restoration is also evolving, with a focus on developing efficient and effective methods for improving image quality. Researchers have explored the use of diffusion-based models, attention mechanisms, and frequency-domain processing to achieve state-of-the-art results. Notable papers include HIMOSA, which proposes a lightweight super-resolution framework, and IRPO, which introduces a low-level GRPO-based post-training paradigm for image restoration. In addition, the field of molecular design and generation is advancing, with a focus on developing innovative methods for generating and optimizing molecular structures. Researchers have highlighted the potential of large language models, graph neural networks, and generative models for advancing the field. Notable papers include Efficient and Programmable Exploration of Synthesizable Chemical Space and Mofasa, which introduces a state-of-the-art generative model for generating metal-organic frameworks. Overall, these advancements have the potential to significantly improve the accuracy and efficiency of models in their respective fields, and are expected to have a major impact on areas such as drug discovery, materials science, and bioinformatics.