# Resilient-native and Intelligent NextG Systems

arXiv: https://arxiv.org/abs/2506.12795 · HTML: https://mlsanity.com/paper/2506.12795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [GFlowNet Foundations](https://mlsanity.com/paper/2111.09266/) (2021)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018)
- [Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach](https://mlsanity.com/paper/2502.01145/) (2025)
- [An STL-based Approach to Resilient Control for Cyber-Physical Systems](https://mlsanity.com/paper/2211.02794/) (2022)
- [Resilient-By-Design: A Resiliency Framework for Future Wireless Networks](https://mlsanity.com/paper/2410.23203/) (2024)
- [An Internal Model Principle For Robots](https://mlsanity.com/paper/2406.11237/) (2024)
- [Semantic Communication meets System 2 ML: How Abstraction, Compositionality and Emergent Languages Shape Intelligence](https://mlsanity.com/paper/2505.20964/) (2025)
- [Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation](https://mlsanity.com/paper/2505.21518/) (2025)
- [Accelerated Recovery with RIS: Designing Wireless Resilience in Mission-Critical Environments](https://mlsanity.com/paper/2504.11589/) (2025)

## Built on by (most cited first, 1 of 1)

- [LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach](https://mlsanity.com/paper/2510.10895/) (2025)

## Abstract (arXiv)

Just like power, water and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless networks must be resilient to unforeseen events, able to withstand and recover from unexpected adverse conditions, shocks, unmodeled disturbances and cascading failures. Despite its critical importance, resilience remains an elusive concept, with its mathematical foundations still underdeveloped. Unlike robustness and reliability, resilience is premised on the fact that disruptions will inevitably happen. Resilience, in terms of elasticity, focuses on the ability to bounce back to favorable states, while resilience as plasticity involves agents (or networks) that can flexibly expand their states, hypotheses and course of actions, by transforming through real-time adaptation and reconfiguration. This constant situational awareness and vigilance of adapting world models and counterfactually reasoning about potential system failures and the corresponding best responses, is a core aspect of resilience. This article seeks to first define resilience and disambiguate it from reliability and robustness, before delving into the mathematics of resilience. Finally, the article concludes by presenting nuanced metrics and discussing trade-offs tailored to the unique characteristics of network resilience.