The field of semantic communications and multimodal processing is moving towards more efficient and effective methods for image transmission, compression, and reconstruction. Researchers are exploring innovative approaches to reduce the computational demands and storage requirements of large AI models, while maintaining or improving their performance. Notable advancements include the development of lightweight deployment strategies, token-based multimodal interactive coding frameworks, and sparse Gaussian representations for dataset distillation. These advancements have the potential to enable more efficient and accurate multimodal processing, and to facilitate the deployment of AI models in resource-constrained environments. Some particularly noteworthy papers in this area include: Large AI Model-Enabled Generative Semantic Communications for Image Transmission, which introduces a novel system for refining semantic granularity in image transmission. UniMIC: Token-Based Multimodal Interactive Coding for Human-AI Collaboration, which proposes a unified token-based multimodal interactive coding framework for efficient low-bitrate transmission. Beyond Pixels: Efficient Dataset Distillation via Sparse Gaussian Representation, which presents a novel sparse representation for dataset distillation based on 2D Gaussians.