The field of in-context learning is rapidly evolving, with a focus on improving the performance and generalization capabilities of large language models. Recent studies have explored the use of task vectors, demonstration selection, and explanation-based approaches to enhance the robustness and adaptability of in-context learning. These innovations have shown promising results, with improvements in task performance, out-of-distribution generalization, and reduced sensitivity to demonstration order and context length constraints. Noteworthy papers in this area include One Task Vector is not Enough, which introduces a novel dataset and reveals the importance of intermediate layers and distributed task knowledge representation, and Exploring Explanations Improves the Robustness of In-Context Learning, which extends X-ICL by systematically exploring explanations for all possible labels. Additionally, Adaptive Task Vectors for Large Language Models proposes a simple and effective framework that dynamically generates task vectors conditioned on each input query, demonstrating strong performance and generalization capabilities.