The field of dynamic graph learning and recommendation systems is rapidly evolving, with a focus on developing more efficient and effective models for capturing complex relationships and temporal dependencies. Recent research has highlighted the importance of incorporating domain-specific knowledge and structural information into graph neural networks, leading to improved performance and generalization capabilities. Notably, attention-free architectures and adaptive token mixing strategies have shown promise in reducing computational complexity while maintaining state-of-the-art performance. Furthermore, the integration of task-aware retrieval augmentation and self-adaptive graph mixture models has enabled more accurate and robust recommendations. Overall, these advances demonstrate a shift towards more nuanced and context-aware modeling approaches in dynamic graph learning and recommendation systems. Noteworthy papers include: MP-GFormer, which introduces a 3D-geometry-aware dynamic graph transformer for machining process planning, achieving significant improvements in accuracy. Dynamic Graph Recommendation via Sparse Augmentation and Singular Adaptation proposes a novel method for efficient fine-tuning in dynamic recommendation systems, achieving state-of-the-art performance on large-scale datasets.