The field of artificial intelligence is moving towards increased transparency and accountability, with a focus on developing methods and techniques for explainable AI and model interpretability. Recent research has made significant progress in this area, with the introduction of new frameworks and approaches for transferring interpretability across language models, analyzing neural networks, and providing compact visual attributions. These advancements have the potential to make AI systems more reliable, controllable, and trustworthy. Noteworthy papers in this area include Atlas-Alignment, which enables the transfer of interpretability across language models, and Extremal Contours, which provides a training-free explanation method for vision models. Additionally, papers such as FineXL and LLEXICORP have made significant contributions to the field of natural language explainability and concept relevance propagation.