The field of mathematical reasoning with large language models (LLMs) is witnessing significant advancements. Researchers are exploring innovative methods to improve the evaluation and generation of mathematical problems, as well as the development of more accurate and reliable assessment frameworks. A notable direction is the focus on multimodal mathematical reasoning, including speech-based models and the integration of vector ontologies to enhance the scalability and interoperability of information artifacts. Furthermore, there is a growing emphasis on holistic evaluation metrics that go beyond final answer accuracy, such as the MAPLE score and SMART framework, to better capture the true problem-solving capabilities of LLMs. Noteworthy papers include: Towards Better Evaluation for Generated Patent Claims, which introduces a comprehensive benchmark for evaluating patent claims and a novel multi-dimensional evaluation method. SMART: Self-Generating and Self-Validating Multi-Dimensional Assessment for LLMs' Mathematical Problem Solving, which presents a framework that decomposes mathematical problem solving into distinct dimensions and enables fine-grained analysis of LLM behavior.