Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

ArXi:2606.04314v1 Announce Type: new As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability. Existing testing methods, whether black-box or white-box, primarily use global mutation or coverage-guided strategies, both of which struggle to efficiently uncover diverse model failures while remaining proximate to the original data distribution and semantics.