# Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2510.00519 · HTML: https://mlsanity.com/paper/2510.00519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2004.05519/) (2020)
- [Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks](https://mlsanity.com/paper/1708.03322/) (2017)
- [A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems](https://mlsanity.com/paper/2005.02979/) (2020)
- [JuliaReach: a Toolbox for Set-Based Reachability](https://mlsanity.com/paper/1901.10736/) (2019)
- [Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management](https://mlsanity.com/paper/1707.00130/) (2017)
- [Reachability Analysis and Safety Verification for Neural Network Control Systems](https://mlsanity.com/paper/1805.09944/) (2018)
- [Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Models: An Approach Based on System Identification](https://mlsanity.com/paper/1910.02837/) (2019)
- [When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward](https://mlsanity.com/paper/2111.04324/) (2021)
- [Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems](https://mlsanity.com/paper/2305.03882/) (2023)
- [AI-in-the-Loop -- The impact of HMI in AI-based Application](https://mlsanity.com/paper/2303.11508/) (2023)
- [AutoRepair: Automated Repair for AI-Enabled Cyber-Physical Systems under Safety-Critical Conditions](https://mlsanity.com/paper/2304.05617/) (2023)
- [Combining Genetic Programming and Model Checking to Generate Environment Assumptions](https://mlsanity.com/paper/2101.01933/) (2021)
- [Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement](https://mlsanity.com/paper/1610.06371/) (2016)
- [Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models](https://mlsanity.com/paper/1905.03490/) (2019)
- [A Literature Review on Blockchain-enabled Security and Operation of Cyber-Physical Systems](https://mlsanity.com/paper/2107.07916/) (2021)

## Abstract (arXiv)

In the world of Cyber-Physical Systems (CPS), a captivating real-time fusion occurs where digital technology meets the physical world. This synergy has been significantly transformed by the integration of artificial intelligence (AI), a move that dramatically enhances system adaptability and introduces a layer of complexity that impacts CPS control optimization and reliability. Despite advancements in AI integration, a significant gap remains in understanding how this shift affects CPS architecture, operational complexity, and verification practices. The extended abstract addresses this gap by investigating architectural distinctions between AI-driven and traditional control models designed in Simulink and their respective implications for system verification.