---
title: "Recovering Formal Guarantees for Neural Network-Controlled Systems Under Changing Dynamics"
authors:
  - "Sterre Lutz"
doi: "10.24963/ijcai.2026/946"
url: "https://ijcai.org/proceedings/2026/946"
pdf: "https://ijcai.org/proceedings/2026/0946.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8335-8336"
track: "Doctoral Consortium"
keywords:
  - "Uncertainty in AI: Sequential decision making"
  - "Agent-based and Multi-agent Systems: Formal verification, validation and synthesis"
  - "AI Ethics, Trust, Fairnes: Safety and robustness"
---

# Recovering Formal Guarantees for Neural Network-Controlled Systems Under Changing Dynamics

Sterre Lutz

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8335-8336. DOI: [10.24963/ijcai.2026/946](https://doi.org/10.24963/ijcai.2026/946)

## Abstract

Neural networks enable high-performance decision-making, but their deployment in safety-critical settings requires formal guarantees that may become invalid after changes in the system dynamics. This research studies how such guarantees can be recovered using information from the initial verification, enabling post-deployment analysis to assess preservation, quantify degradation, and restore compliant behavior without recomputing verification from scratch.