Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G

Liang Wu, Kelly Wan, Mayank Darbari, Liangjie Hong
5/20/2026
cs.AIcs.LG

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Abstract

The proliferation of emerging applications, such as autonomous driving and immersive experiences, demands cellular networks that are not only faster, but fundamentally more resilient and autonomous. This paper presents a BlueSky vision on how Artificial Intelligence will be natively integrated into 6G, shifting the paradigm from \underline{Network for AI} to \underline{AI for Network}. We envision that, unlike 5G's reliance on scattered, ad-hoc models each trained for a single task, native AI in the 6G era will be anchored by a foundation model and and orchestrated via collaborative multi-agent systems, framing network management as a unified, multi-modal, multi-task optimization problem. Built on this vision, we outline two transformative directions. The first focuses on developing a 6G foundation model as a unified backbone, with task-specific knowledge distilled into compact models suited for diverse edge deployments. The second advances multi-agent systems designed to autonomously diagnose, maintain, and recover networks with minimal human intervention. These directions chart a roadmap for 6G to evolve into an intelligent, self-sustaining communication infrastructure.

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@article{wu2026towards,
  title  = {Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G},
  author = {Liang Wu and Kelly Wan and Mayank Darbari and Liangjie Hong},
  year   = {2026},
  eprint = {2605.21395},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2605.21395v1}
}

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