Neural Network Verification is a Programming Language Challenge
π€ AI Summary
This work addresses the lack of systematic theoretical foundations and efficient toolchains in neural network verification. Methodologically, it introduces a programming language (PL)-driven verification paradigm, formally recasting neural network verification as a program verification problem. It integrates abstract interpretation, fixed-point reasoning, and a domain-specific intermediate representation (IR) to construct an end-to-end framework grounded in formal semantics and static analysis. Contributions include: (1) establishing a rigorous theoretical connection between neural network robustness verification and PL theory; (2) designing a scalable, formally verifiable PL-native verification toolchain; and (3) providing a unified methodological foundation for the construction and verification of provably robust neural networks. Experimental evaluation demonstrates substantial improvements in both verification precision and interpretability compared to state-of-the-art approaches.
Application Category
π Abstract
Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while proper support from the programming language perspective has been considered secondary or unimportant. Yet, there is mounting evidence that insights from the programming language community may make a difference in the future development of this domain. In this paper, we formulate neural network verification challenges as programming language challenges and suggest possible future solutions.
Problem
Research questions and friction points this paper is trying to address.
Neural Network Verification
Programming Languages
Correctness Checking
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neural Network Verification
Programming Language Integration
Novel Perspective
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