The field of natural language processing is moving towards more advanced and innovative methods for text generation and style transfer. Recent developments have shown a focus on improving the quality and diversity of generated text, with a particular emphasis on leveraging inference-time scaling and diffusion models. These approaches have demonstrated significant gains in generation quality and have the potential to revolutionize the field. Notable papers in this area include those that propose new frameworks for stylized 3D morphable face models and text augmentation paradigms. Overall, the field is advancing rapidly, with new methods and techniques being proposed to improve the state-of-the-art in text generation and style transfer. Noteworthy papers include: Improving Text Style Transfer using Masked Diffusion Language Models with Inference-time Scaling, which proposes a verifier-based inference-time scaling method for masked diffusion language models, and StyleMM, which introduces a novel framework for constructing stylized 3D morphable face models via text-driven aligned image translation.