The field of artificial intelligence is witnessing significant advancements in the development and application of large language models (LLMs) in multi-agent systems and tool orchestration. Recent studies have focused on enhancing the capabilities of LLMs to interact with external interfaces, select optimal models, and reason across different applications. The integration of LLMs with neuro-symbolic frameworks and ontology-enhanced methods has shown promise in improving multi-intent understanding and reducing false positives in vulnerability management. Furthermore, the development of benchmarks such as AppSelectBench and Tool-RoCo has facilitated the evaluation of LLMs in application selection and multi-agent cooperation. Noteworthy papers include A Needle in a Haystack, which proposes a feature tree-guided recommendation framework to improve the precision and efficiency of LLMs, and ToolOrchestra, which introduces a method for training small orchestrators to coordinate intelligent tools and achieve higher accuracy at lower cost. Additionally, HuggingR$^4$ presents a progressive reasoning framework for discovering optimal model companions, and NOEM$^3$A introduces a neuro-symbolic ontology-enhanced method for multi-intent understanding in mobile agents. These advancements have the potential to enable more efficient and effective tool-augmented reasoning systems and pave the way for practical and scalable applications.