The field of computer vision and natural language processing is moving towards developing more interpretable and robust models. Recent research has focused on improving the representational power of sparse autoencoders in vision models, enabling controllable generation and out-of-distribution generalization. Additionally, there have been significant advancements in image restoration techniques, including the development of efficient and temporally consistent video restoration methods and moiré pattern removal algorithms. These innovations have the potential to improve the performance and safety of large language models and computer vision systems. Noteworthy papers include: Probing the Representational Power of Sparse Autoencoders in Vision Models, which demonstrates the effectiveness of sparse autoencoders in improving interpretability and generalization in vision models. RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigator, which proposes a lightweight recurrent framework for efficient and temporally consistent video restoration. CorrSteer: Steering Improves Task Performance and Safety in LLMs through Correlation-based Sparse Autoencoder Feature Selection, which introduces a correlation-based feature selection method for sparse autoencoders. MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing, which presents a novel transformer-based approach for moiré pattern removal in videos. MF-LPR^2: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow, which proposes a multi-frame license plate restoration and recognition framework using optical flow. CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction, which presents a curriculum-guided masked autoencoder for remote sensing data reconstruction. Evaluating Sparse Autoencoders for Monosemantic Representation, which provides a systematic evaluation of sparse autoencoders for monosemantic representation. From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations, which investigates how masked autoencoders learn spatial correlations in input images.