The field of large language models and deep learning is moving towards more efficient and scalable solutions. Researchers are exploring various techniques to reduce computational costs and improve model performance, such as sparse attention mechanisms, structured pruning, and synaptic pruning. These methods have shown promising results in improving model accuracy and reducing computational demands. Notably, some studies have demonstrated that sparsity can act as a powerful regularizer, preventing overfitting and improving generalization. The integration of economic principles and graph theory into model design is also a growing trend, enabling the development of more efficient and adaptive models. Noteworthy papers include: Crisp Attention, which introduces structured sparsity to the attention mechanism, resulting in improved model accuracy. Synaptic Pruning, which proposes a magnitude-based pruning method that outperforms standard dropout techniques in time series forecasting tasks. EGGS-PTP, which leverages graph theory to guide structured pruning, achieving significant acceleration and memory savings in large language models.