The field of natural language processing is witnessing significant developments in language modeling and text generation. Researchers are exploring alternative approaches to traditional autoregressive models, such as masked diffusion models and multi-agent frameworks, to improve the coherence and quality of generated text. These new methods are showing promise in addressing long-standing challenges in text generation, including discourse coherence and narrative complexity. Furthermore, advancements in latent space representation and compression are enabling more efficient and effective text generation. Noteworthy papers in this area include: StoryWriter, which proposes a multi-agent story generation framework that significantly outperforms existing baselines. Plan for Speed introduces a dilated scheduling method for masked diffusion language models, achieving substantial speedups over state-of-the-art models. Any-Order GPT as Masked Diffusion Model decouples the formulation and architecture of masked diffusion models, offering insights for future model design. ReCode updates code API knowledge with reinforcement learning, substantially boosting LLMs' code generation performance in dynamic API scenarios. DiffuCoder analyzes the decoding behavior of diffusion large language models, revealing new insights into their generation process and offering an effective training framework. Instella-T2I pushes the limits of 1D discrete latent space image generation, achieving competitive performance with a significant reduction in token numbers. Compressed and Smooth Latent Space for Text Diffusion Modeling introduces Cosmos, a novel approach to text generation that operates in a compressed, smooth latent space, enabling faster inference and comparable generation quality.