The field of large language models (LLMs) and urban space analysis is rapidly evolving, with a focus on improving the reasoning capabilities of LLMs and integrating them with multimodal data from urban spaces. Recent developments have centered on creating frameworks that incorporate domain-specific best practices into LLMs, enabling more fine-grained control over their behavior. Additionally, there is a growing interest in using LLMs for semantic integration of knowledge graphs in urban spaces, allowing for the identification and reasoning about incidents and events. Another area of research is the application of LLMs in distributed edge computing frameworks for smart city digital twins, which enables efficient operation and adaptive decision-making. Noteworthy papers in this area include SHERPA, which proposes a model-driven framework for LLM execution, and SIGMUS, which introduces a system for semantic integration of knowledge graphs in multimodal urban spaces. UrbanInsight is also a notable work, presenting a distributed edge computing framework with LLM-powered data filtering for smart city digital twins. Furthermore, Counterfactual Sensitivity Regularization is a promising approach for improving the faithfulness of LLMs, while Implicit Reasoning in Large Language Models provides a comprehensive survey of the mechanisms and execution paradigms underlying implicit reasoning in LLMs. Other notable works include Curse of Knowledge, which investigates biases in LLM judges, and Inverse IFEval, which proposes a benchmark for evaluating the ability of LLMs to unlearn stubborn training conventions. Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models is also a significant contribution, introducing a framework for bidirectional reasoning and genuine understanding in LLMs.