Neural Network Verification is a Programming Language Challenge

2025-01-10

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.

Technology Category

Natural Language Processing: Safety and RobustnessComputer Vision: Adversarial Attacks & RobustnessMachine Learning: Adversarial Learning & Robustness

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsResponsible Web: Human-perceived consequences of algorithmic deployment on the web

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

• Neural Network Verification
• Programming Languages
• Correctness Checking

Innovation

• Neural Network Verification
• Programming Language Integration
• Novel Perspective