The recent advancements in the research area primarily focus on enhancing the efficiency and performance of large-scale models, particularly in the context of multimodal and language models. Key innovations include novel pruning techniques that aim to reduce computational and memory costs without significantly compromising model accuracy. These methods often leverage adaptive strategies, such as cross-modality attention decomposition and unit-wise retention probabilities, to more precisely identify and prune irrelevant tokens or parameters. Additionally, there is a growing emphasis on developing training-free or post-training methods that can be applied directly to pre-trained models, thereby simplifying the deployment process. These approaches not only improve inference speed and resource utilization but also demonstrate significant improvements in accuracy and energy efficiency. Furthermore, the integration of binarization and early exit mechanisms into transformer architectures has shown promising results in reducing model size and computational complexity while maintaining or even enhancing performance. The field is also witnessing advancements in hardware-accelerated non-linearities and flexible, plug-and-play modules for model optimization, which are crucial for the practical deployment of these models in resource-constrained environments. Overall, the research is moving towards more efficient, adaptable, and scalable solutions that balance performance and resource constraints, paving the way for broader applications in various domains.