Building future wireless systems that support services like digital twins
(DTs) is challenging to achieve through advances to conventional technologies
like meta-surfaces. While artificial intelligence (AI)-native networks promise
to overcome some limitations of wireless technologies, developments still rely
on AI tools like neural networks. Such tools struggle to cope with the
non-trivial challenges of the network environment and the growing demands of
emerging use cases. In this paper, we revisit the concept of AI-native wireless
systems, equipping them with the common sense necessary to transform them into
artificial general intelligence (AGI)-native systems. These systems acquire
common sense by exploiting different cognitive abilities such as perception,
analogy, and reasoning, that enable them to generalize and deal with unforeseen
scenarios. Towards developing the components of such a system, we start by
showing how the perception module can be built through abstracting real-world
elements into generalizable representations. These representations are then
used to create a world model, founded on principles of causality and
hyper-dimensional (HD) computing, that aligns with intuitive physics and
enables analogical reasoning, that define common sense. Then, we explain how
methods such as integrated information theory play a role in the proposed
intent-driven and objective-driven planning methods that maneuver the
AGI-native network to take actions. Next, we discuss how an AGI-native network
can enable use cases related to human and autonomous agents: a) analogical
reasoning for next-generation DTs, b) synchronized and resilient experiences
for cognitive avatars, and c) brain-level metaverse experiences like
holographic teleportation. Finally, we conclude with a set of recommendations
to build AGI-native systems. Ultimately, we envision this paper as a roadmap
for the beyond 6G era.

提出了 AI 本地化无线系统的概念，通过赋予其必要的常识，向人工通用智能系统转化。通过感知模块和类比推理等认知能力，构建了系统的认知模型，并探讨了其在下一代 DT、认知化人工身影和大脑级元宇宙体验等应用方面的潜力。