arXiv:2605.20482v1 Announce Type: new Abstract: Quadratic constraints (QCs) are widely used to characterize nonlinearities and uncertainties, but generic analytical characterizations can be conservative on bounded domains. This paper develops a framework for constructing verified quadratic characterizations of scalar relations in the two-dimensional real plane. Candidate quadratic inequalities are locally generated by solving convex quadratic programs using samples from the relation and exterior sample points. They are then verified globally using sum-of-squares certificates over an exact semi
The paper tackles a critical challenge in AI safety and robustness—the verified characterization of neural network behavior—which is gaining urgency with the deployment of AI in sensitive applications.
This research provides a more precise and less conservative method for analyzing the reachability and safety of neural networks, crucial for their reliable operation in real-world systems.
The ability to generate and verify stricter quadratic characterizations of neural networks offers a pathway to more trustworthy AI systems, particularly in domains where formal verification is essential.
- · AI safety researchers
- · Autonomous systems developers
- · AI verification tool vendors
- · Developers relying solely on empirical testing
Improved methods for ensuring the safety and reliability of neural networks, reducing uncertainty in their behavior.
Accelerated adoption of AI in safety-critical applications like autonomous vehicles and industrial control systems due to enhanced trustworthiness.
Potential for new regulatory frameworks and certification processes for AI systems based on formal verification techniques.
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Read at arXiv cs.LG