Future wireless communication networks are in a position to move beyond
data-centric, device-oriented connectivity and offer intelligent, immersive
experiences based on task-oriented connections, especially in the context of
the thriving development of pre-trained foundation models (PFM) and the
evolving vision of 6G native artificial intelligence (AI). Therefore,
redefining modes of collaboration between devices and servers and constructing
native intelligence libraries become critically important in 6G. In this paper,
we analyze the challenges of achieving 6G native AI from the perspectives of
data, intelligence, and networks. Then, we propose a 6G native AI framework
based on foundation models, provide a customization approach for intent-aware
PFM, present a construction of a task-oriented AI toolkit, and outline a novel
cloud-edge-end collaboration paradigm. As a practical use case, we apply this
framework for orchestration, achieving the maximum sum rate within a wireless
communication system, and presenting preliminary evaluation results. Finally,
we outline research directions for achieving native AI in 6G.

通过基于基础模型的六代移动通信系统本地人工智能框架，在任务导向连接方面实现智能且沉浸式的体验，重新定义设备和服务器之间的协作模式，构建本地智能库，并提出了一种定制化方法和一种基于任务的人工智能工具包的建构，同时提出了一种新颖的云边缘协同模式。