The field of large language models (LLMs) is rapidly advancing, with a focus on developing more sophisticated and reliable models for complex problem-solving. Recent research has explored the application of LLMs in various domains, including mathematical modeling, ecological modeling, and educational scenarios. A key direction in this field is the development of models that can effectively interact with external tools and systems, enabling more efficient and accurate solutions to real-world problems. Notable advancements include the creation of novel programming languages and frameworks for LLM orchestration, such as Pel, and the development of benchmarks and evaluation metrics for assessing LLM performance in complex tasks. Overall, the field is moving towards more integrated and interdisciplinary approaches, combining LLMs with other AI technologies and domain-specific expertise to tackle challenging problems. Noteworthy papers include LongFuncEval, which investigates the effectiveness of LLMs in long context settings, and MM-Agent, which proposes a framework for LLM-powered mathematical modeling. Additionally, ModelingAgent and MCP-RADAR introduce new benchmarks and evaluation methodologies for assessing LLM performance in real-world scenarios.