A New Strategy for Verifying Reach-Avoid Specifications in Neural Feedback Systems

Fuente: arXiv
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Autori principali: Akinwande, Samuel I., Katz, Sydney M., Kochenderfer, Mykel J., Barrett, Clark
Natura: Preprint
Pubblicazione: 2026
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author Akinwande, Samuel I.
Katz, Sydney M.
Kochenderfer, Mykel J.
Barrett, Clark
author_facet Akinwande, Samuel I.
Katz, Sydney M.
Kochenderfer, Mykel J.
Barrett, Clark
contents Forward reachability analysis is the predominant approach for verifying reach-avoid properties in neural feedback systems (dynamical systems controlled by neural networks). This dominance stems from the limited scalability of existing backward reachability methods. In this work, we introduce new algorithms that compute both over- and under-approximations of backward reachable sets for such systems. We further integrate these backward algorithms with established forward analysis techniques to yield a unified verification framework for neural feedback systems.
format Preprint
id arxiv_https___arxiv_org_abs_2601_08065
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A New Strategy for Verifying Reach-Avoid Specifications in Neural Feedback Systems
Akinwande, Samuel I.
Katz, Sydney M.
Kochenderfer, Mykel J.
Barrett, Clark
Artificial Intelligence
Forward reachability analysis is the predominant approach for verifying reach-avoid properties in neural feedback systems (dynamical systems controlled by neural networks). This dominance stems from the limited scalability of existing backward reachability methods. In this work, we introduce new algorithms that compute both over- and under-approximations of backward reachable sets for such systems. We further integrate these backward algorithms with established forward analysis techniques to yield a unified verification framework for neural feedback systems.
title A New Strategy for Verifying Reach-Avoid Specifications in Neural Feedback Systems
topic Artificial Intelligence
url https://arxiv.org/abs/2601.08065