The field of geospatial and environmental machine learning is witnessing significant advancements, driven by the development of more robust and generalizable models for real-world applications. A common theme across recent research is the emphasis on benchmarking and evaluating models on diverse, high-impact tasks and domains. Notable benchmarks include ExEBench, which introduces a benchmark for extreme earth events, and FedRS-Bench, which provides a realistic federated dataset and benchmark for remote sensing. Additionally, a large-scale benchmark on geological fault delineation models has been proposed, systematically assessing pretraining, fine-tuning, and joint training strategies under varying degrees of domain shift. The integration of large language models (LLMs) is also transforming the field of geospatial analysis and energy management. LLMs are enabling the development of more robust and generalizable models that can operate with limited labeled data, enhancing interpretability and reducing the need for fine-tuning. A study introduced a prompt-based NILM framework using LLMs, achieving competitive state detection accuracy and robust generalization without fine-tuning. Another paper proposed CartoAgent, a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation, demonstrating the effectiveness of LLMs in generating visually appealing and informative maps. Furthermore, researchers are exploring innovative ways to incorporate external knowledge into LLMs, including using reinforcement learning to optimize search usage and integrating structural entropy-guided knowledge navigation. The development of DynamicRAG, a framework that dynamically adjusts the order and number of retrieved documents based on the query, and InForage, a reinforcement learning framework that formalizes retrieval-augmented reasoning as a dynamic information-seeking process, are notable contributions in this area. The field of large language models is also moving towards more interactive and multimodal learning approaches, with researchers exploring ways to integrate LLMs with reinforcement learning. This shift is driven by the need for more effective and efficient learning methods, particularly in domains where data is scarce or difficult to obtain. Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains presents a framework for efficient posterior sampling with LLM-derived priors. MLE-Dojo introduces an interactive environment for systematically reinforcement learning and evaluating LLM agents. Self Rewarding Self Improving demonstrates that LLMs can effectively self-improve through self-judging without requiring reference solutions. Lastly, significant advancements are being made in tool integration, enabling LLMs to interact dynamically with external tools and APIs. Novel approaches, such as dynamic tool selection and self-improving frameworks, are being developed to enhance the tool-using capabilities of LLMs. ScaleMCP, ToolACE-DEV, and TUMS are notable frameworks in this area, demonstrating the progress being made towards improving the performance and autonomy of LLMs in various applications.