The field of natural language processing is moving towards more efficient and scalable methods for large language models (LLMs) to invoke external tools and retrieve relevant information. Researchers are exploring novel frameworks and architectures to address the challenges of tool selection and retrieval, particularly in cases where the tool repository is rapidly expanding and includes unseen tools. One of the key directions is the development of probabilistic pruning methods and logic-guided semantic bridging frameworks, which aim to improve tool selection accuracy and reduce inference latency. Another area of focus is the exploration of complex ontologies, with the development of fuzzy ontology embeddings and visual query building tools that enable intuitive and expressive exploration of large, complex ontologies. Noteworthy papers include: HGMF, which proposes a hierarchical Gaussian mixture framework for scalable tool invocation, and LoSemB, which introduces a logic-guided semantic bridging framework for inductive tool retrieval. FuzzyVis is also a notable system that enables intuitive and expressive exploration of complex ontologies using fuzzy logic-based querying and interactive visual interfaces.