The field of natural language processing is witnessing a significant shift towards developing more efficient and interpretable transformer architectures. Recent developments suggest that sparse attention mechanisms and novel positional encoding designs can greatly improve long-context generalization capabilities. Furthermore, there is a growing interest in designing frameworks that provide more transparent and explainable models, such as language bottleneck models and statistical frameworks for relative contextualization. These advancements have the potential to enhance the accuracy and trustworthiness of large language models. Notable papers in this area include: Language Bottleneck Models, which introduces a framework for interpretable knowledge tracing by learning the minimum natural-language summary that makes past answers explainable and future answers predictable. NaLaFormer, which proposes a novel Norm-Aware Linear Attention mechanism that restores norm-guided dynamic spikiness and recovers kernel-perturbed norm distributions, leading to improved performance on vision and language tasks.