The field of large language models (LLMs) is rapidly advancing, with a focus on enhancing their capabilities through tool learning and integration. Recent developments have explored the use of LLMs in various domains, including visual reasoning, code generation, and programming assignment grading. A common theme among these advancements is the need for more efficient and effective methods of tool invocation, retrieval, and simulation. Researchers are investigating novel approaches, such as discrepancy-aware workflow generation, instruct-masking fine-tuning, and chain-of-thought prompting, to improve the performance and interpretability of LLMs. Noteworthy papers in this area include DWIM, which proposes a tool-aware visual reasoning approach, and StepGrade, which introduces a context-aware LLM for grading programming assignments. Additionally, papers like CodeTool and AllianceCoder have demonstrated significant improvements in LLM performance through the use of process supervision and context-integrated methods. Overall, the field is moving towards more sophisticated and flexible LLMs that can effectively leverage tools and external knowledge to tackle complex tasks.