The field of multimodal generation is rapidly advancing, with a focus on improving the quality and controllability of generated outputs. Researchers are exploring new frameworks and models that can effectively capture and summarize visual and structural elements, enabling applications such as chart-to-code generation and artistic glyph image generation. Additionally, there is a growing emphasis on using AI to bridge the accessibility gap, particularly for individuals with vision impairment, through the generation of tactile graphics. Noteworthy papers in this area include: AnyArtisticGlyph, which introduces a diffusion-based model for multilingual controllable artistic glyph generation, TactileNet, which presents a comprehensive dataset and AI-driven framework for generating tactile graphics, OmniSVG, which proposes a unified framework for end-to-end multimodal SVG generation.