The field of large language models (LLMs) is rapidly evolving, with a focus on improving their ability to simulate human-like behavior, personalize interactions, and adapt to diverse applications. Recent developments have introduced innovative methods for controlling LLM behavior, such as action-aware persona modeling, activation steering, and physics steering, which enable more realistic and effective simulations. Additionally, advancements in online learning, sparse feature selection, and probabilistic hash embeddings have enhanced the capabilities of LLMs in handling complex data streams and categorical features. Noteworthy papers in this area include: Point of Order, which introduces a reproducible pipeline for transforming public Zoom recordings into speaker-attributed transcripts with metadata, enabling more realistic civic simulations. MTA, which proposes a Merge-then-Adapt framework for personalized large language models, achieving state-of-the-art performance across multiple tasks.