The field of image and language processing is witnessing significant advancements with the integration of novel architectures and techniques. Recent developments have focused on improving the efficiency and accuracy of image super-resolution, multi-view stereo, and differentially private text rewriting. The incorporation of frequency-aware state-space models, diffusion transformers, and multi-level wavelet spectra has led to superior performance in image super-resolution tasks. Furthermore, the application of Mamba-based architectures has enabled efficient global feature aggregation in multi-view stereo methods. In the realm of natural language processing, differentially private in-context learning has become a prominent area of research, with a focus on developing privacy-aware nearest neighbor search frameworks. Noteworthy papers include: MVSMamba, which proposes a Mamba-based multi-view stereo network with a dynamic module for efficient feature interaction, Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution, which introduces a diffusion transformer model that captures interrelations among multiscale frequency sub-bands for improved image super-resolution, Differentially Private In-Context Learning with Nearest Neighbor Search, which presents a framework for differentially private in-context learning that integrates nearest neighbor search in a privacy-aware manner.