The field of content-aware layout generation and text visualization is moving towards more innovative and effective methods for arranging design elements and conveying information. Recent developments have focused on leveraging large language models and vision language models to generate layouts and designs that are more aesthetically coherent and aligned with human aesthetics. Notable progress has been made in addressing the limitations of existing methods, such as the inability to adequately interpret spatial relationships among visual themes and design elements. The use of relation reasoning and top-down approaches has shown promising results in generating more structured and diverse layouts. Furthermore, advances in text visualization have enabled the rendering of structured text with style, allowing for more effective conveyance of semantic information. However, concerns regarding the privacy of text embeddings have also been raised, highlighting the need for caution and further research into robust defense mechanisms. Some notable papers in this area include: ReLayout, which introduces a novel method for content-aware layout generation that leverages relation reasoning to generate more reasonable and aesthetically coherent layouts. Accordion, a graphic design generation framework that takes a top-down approach to convert AI-generated designs into editable layered designs. Ragged Blocks, a layout algorithm that generates rectilinear polygons to compactly render nested text with minimal disturbance to the underlying typographic layout.