arXiv:2605.29537v1 Announce Type: cross Abstract: We investigate the computational complexity of neural network verification in quantised settings. We distinguish three classes of Feedforward Neural Networks (FNNs): rational FNNs with exact rational weights, quantised FNNs whose weights come from a finite-width arithmetic, and dynamically quantised FNNs in which rational networks are evaluated with respect to a given finite-width arithmetic. We consider two types of specifications used in the literature. Linear programming (LP) specifications are conjunctions of linear constraints, while bit-v
The increasing deployment of neural networks in safety-critical applications necessitates deeper understanding of their verification complexity, especially as quantization becomes prevalent for efficiency.
This research provides foundational insights into the computability and complexity tradeoffs of verifying neural networks, impacting the reliability and safety of AI systems across various industries.
Our understanding of the practical limits and theoretical challenges in ensuring robust and safe AI systems, particularly those using quantized models, is now more refined.
- · AI Safety Researchers
- · Hardware Manufacturers
- · Certification Bodies
- · Developers of unverified AI applications
- · AI systems with opaque safety guarantees
Improved methods and tools for verifying the behavior and safety of AI models will emerge.
Increased trust and adoption of verified AI systems in regulated and safety-critical domains.
New industry standards and regulatory frameworks for AI system certification based on verifiable properties.
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Read at arXiv cs.LG