The field of neural representation learning and generation is rapidly advancing, with a focus on developing more efficient, scalable, and versatile methods. Recent work has explored the potential of weight space representation learning, structured diffusion models, and latent field representations to improve the quality and coherence of generated data. These approaches have shown promising results in various applications, including 3D asset generation, text-to-intrinsic generation, and lighting representation. Notably, the use of pre-trained base models, low-rank adaptation, and tensor-based adaptations has enabled more effective and efficient learning. Furthermore, the integration of semantic priors and asynchronous latent diffusion has led to improved texture generation and faster convergence. Overall, the field is moving towards more unified and structured representations, enabling better cross-modal transfer and more realistic generated data. Noteworthy papers include: LumiX, which achieves coherent text-to-intrinsic generation through structured diffusion, and LaFiTe, which introduces a generative latent field for 3D native texturing.