In the rapid development of artificial intelligence, solving complex AI tasks is a crucial technology in intelligent mobile networks. Despite the good performance of specialized AI models in intelligent mobile networks, they are unable to handle complicated AI tasks. To address this challenge, we propose Systematic Artificial Intelligence (SAI), which is a framework designed to solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format intent-based input to connect self-designed model library and database. Specifically, we first design a multi-input component, which simultaneously integrates Large Language Models (LLMs) and JSON-format intent-based inputs to fulfill the diverse intent requirements of different users. In addition, we introduce a model library module based on model cards which employ model cards to pairwise match between different modules for model composition. Model cards contain the corresponding model's name and the required performance metrics. Then when receiving user network requirements, we execute each subtask for multiple selected model combinations and provide output based on the execution results and LLM feedback. By leveraging the language capabilities of LLMs and the abundant AI models in the model library, SAI can complete numerous complex AI tasks in the communication network, achieving impressive results in network optimization, resource allocation, and other challenging tasks.

翻译：在人工智能的快速发展中，解决复杂AI任务是智能移动网络中的关键技术。尽管专用AI模型在智能移动网络中表现良好，但难以处理复杂的AI任务。为解决这一挑战，我们提出系统性人工智能（SAI），该框架通过利用大语言模型（LLM）和基于JSON格式意图的输入，连接自设计模型库与数据库，从而求解AI任务。具体而言，我们首先设计了一个多输入组件，该组件同时集成大语言模型（LLM）和基于JSON格式意图的输入，以满足不同用户的多样化意图需求。此外，我们引入基于模型卡的模型库模块，该模块采用模型卡对不同模块之间进行配对匹配以实现模型组合。模型卡包含对应模型的名称及所需性能指标。当接收到用户网络需求时，我们对选定的多种模型组合分别执行各子任务，并根据执行结果与LLM反馈生成输出。通过利用LLM的语言能力与模型库中丰富的AI模型，SAI能够完成通信网络中大量复杂AI任务，在网络优化、资源分配及其他挑战性任务中取得了显著成效。