The field of image generation and visual reasoning is rapidly evolving, with a focus on developing more sophisticated and human-like models. Recent research has explored the use of reinforcement learning, multimodal large language models, and novel evaluation frameworks to improve the quality and diversity of generated images. One of the key challenges in this area is the trade-off between different dimensions, such as quality, alignment, diversity, and robustness, and researchers are working to develop more comprehensive and nuanced evaluation metrics. Noteworthy papers in this area include: Enhancing Reward Models for High-quality Image Generation, which proposes a novel evaluation score to assess the degree to which images represent textual content. Learning Only with Images, which introduces a framework for visual reinforcement learning with reasoning, rendering, and visual feedback. Multimodal LLMs as Customized Reward Models for Text-to-Image Generation, which leverages pretrained multimodal large language models to automatically evaluate text-to-image generations. X-Omni, which demonstrates that reinforcement learning can effectively mitigate artifacts and enhance the generation quality of discrete autoregressive modeling methods. Trade-offs in Image Generation, which explores the complex trade-offs among different dimensions in image generation and proposes a benchmark and evaluation metric to quantify these trade-offs. ScreenCoder, which advances visual-to-code generation for front-end automation via modular multimodal agents. SMART-Editor, which presents a multi-agent framework for human-like design editing with structural integrity.