The field of All-in-One Image Restoration (AiOR) is witnessing significant advancements with the development of novel generative approaches and improved latent space representations. Researchers are focusing on enhancing the efficiency and generalizability of AiOR methods, enabling them to handle multiple types of degradations simultaneously and perform well in real-world scenarios. Notably, the use of visual autoregressive modeling and multi-source representation learning frameworks has shown promising results in improving restoration performance and reducing computational costs. Furthermore, domain adaptation strategies and cross-domain degradation pattern matching are being explored to bridge the gap between controlled scenarios and real-world applications. These innovative approaches are paving the way for more robust and efficient AiOR methods. Noteworthy papers include: RestoreVAR, which proposes a novel generative approach for AiOR that achieves state-of-the-art performance while reducing inference time. BaryIR, which introduces a multi-source representation learning framework that enables unified feature encoding and source-specific semantic encoding for generalizable AiOR. UDAIR, which presents a unified domain-adaptive image restoration framework that achieves new state-of-the-art performance for AiOR by leveraging learned knowledge from source to target domains. D-AR, which recasts the image diffusion process as a vanilla autoregressive procedure, enabling consistent previews and zero-shot layout-controlled synthesis.