The field of multimodal large language models (MLLMs) and visual text processing is rapidly evolving, with a focus on improving visual comprehension and text rendering capabilities. Recent developments have explored the use of unsupervised methods for chain-of-thought reasoning, allowing for more accurate and flexible visual text understanding. Additionally, innovations in text-to-image generation models have enabled the precise rendering of multilingual visual text, while diffusion-based methods have been used to generate high-quality font images. The selection of visual layers in MLLMs has also been reexamined, with findings suggesting that a combination of shallow, middle, and deep layers can achieve better performance across various tasks. Furthermore, comprehensive reviews and unified evaluations of visual text processing have highlighted the need for more robust models that can effectively capture and leverage distinct textual characteristics. Noteworthy papers include: Unsupervised Visual Chain-of-Thought Reasoning via Preference Optimization, which introduced a novel framework for image-level CoT reasoning via preference optimization. RepText, which empowered pre-trained monolingual text-to-image generation models to accurately render multilingual visual text. Text-Conditioned Diffusion Model for High-Fidelity Korean Font Generation, which generated high-quality, diverse Korean font images using only a single reference image. Rethinking Visual Layer Selection in Multimodal LLMs, which proposed a Layer-wise Representation Similarity approach to group CLIP-ViT layers with similar behaviors. Visual Text Processing: A Comprehensive Review and Unified Evaluation, which presented a comprehensive analysis of recent advancements in visual text processing and introduced VTPBench, a new benchmark for evaluating visual text processing models.