The field of natural language processing is witnessing significant developments in prompt optimization and explainability for large language models. Researchers are exploring innovative methods to improve the performance of these models by designing effective prompts and understanding their decision-making processes. One notable trend is the development of automatic prompt optimization techniques, which aim to reduce the manual effort required to craft high-quality prompts. These methods have shown promising results in enhancing the performance of large language models across various tasks. Another area of focus is explainability, with researchers proposing techniques to provide insights into the model's reasoning and decision-making processes. This includes visualizing the salient regions of input data that the model relies on to generate responses. These advancements have the potential to improve the overall performance and trustworthiness of large language models, enabling their safe and effective deployment in real-world applications. Noteworthy papers in this area include AutoV, which learns to automatically select the optimal visual prompt for large vision-language models, and RiOT, a novel framework for efficient prompt refinement with residual optimization tree. RATTPO is also notable for its reward-agnostic test-time prompt optimization method, allowing for flexible and efficient optimization across various reward scenarios.