The field of GeoAI and large language models is rapidly evolving, with a growing emphasis on open-source solutions, modular machine learning, and the integration of quantum theory. Researchers are exploring new approaches to improve the capabilities of large language models, including the use of categorical semantics and symbolic reasoning. The application of these models in urban planning and transportation electrification is also being investigated, with a focus on ensuring accountable planning and human oversight. Notable papers in this area include the introduction of Modular Machine Learning as a novel learning paradigm, which aims to enhance the capability of large language models in counterfactual reasoning and mitigating hallucinations. The UrbanPlanBench benchmark is also a significant contribution, evaluating the efficacy of large language models in urban planning and highlighting the need for further improvement in domain-specific terminology and reasoning. Additionally, the Theoretical Foundations for Semantic Cognition in Artificial Intelligence presents a modular cognitive architecture for artificial intelligence grounded in the formal modeling of belief as structured semantic state, offering a foundational substrate for constructing agents that reason, remember, and regulate their beliefs in structured, interpretable ways.