The field of multimodal recommendation systems is witnessing a shift towards more sophisticated and efficient models that address the limitations of traditional Graph Convolutional Networks (GCNs). Recent advancements emphasize the importance of integrating multimodal data while mitigating the over-smoothing issue inherent in GCNs. Innovations such as topology-aware Multi-Layer Perceptrons (MLPs) and modality-independent Graph Neural Networks (GNNs) are being explored to enhance representation power and capture complex correlations. Additionally, there is a growing focus on reducing modality-specific noise and integrating global information through novel fusion techniques and global transformers. These developments aim to improve both the performance and robustness of recommendation systems, particularly in e-commerce scenarios where user behaviors are influenced by a combination of collaborative and multimodal information. Notably, approaches like Topology-aware MLPs and Spectrum-based Modality Representation Fusion are setting new benchmarks in multimodal recommendation, demonstrating significant improvements in training efficiency and recommendation accuracy.