The field of networking and large language models is experiencing significant growth, with a focus on optimizing performance, improving anonymity, and enhancing decision-making capabilities. Researchers are exploring the use of large language models to automatically optimize congestion control algorithms, formulate stochastic optimization problems, and adapt programmatic workflows to data. Additionally, there is a growing interest in developing congestion-aware multihoming algorithms and queue management strategies for named data networking. These advancements have the potential to significantly improve network performance, security, and efficiency. Noteworthy papers include: Congestion Control System Optimization with Large Language Models, which introduces a novel approach to optimizing congestion control algorithms using large language models, achieving up to 27% performance improvements. Type-Compliant Adaptation Cascades, which presents a framework for adapting programmatic large language model workflows to data, significantly outperforming state-of-the-art prompt-optimization baselines.