Bridging the Semantic Gap in Neural Network Execution and Verification for Safety-Critical Systems

Bridging the Semantic Gap in Neural Network Verification: A Critical Analysis of TorchLean's Approach 1. The Semantic Gap: A Looming Threat to Safety-Critical Systems The increasing integration of neural networks into safety-critical systems, such as autonomous vehicles and medical devices, demands rigorous verification of their behavior. However, a persistent challenge, known as the semantic gap , undermines the reliability of these guarantees. This gap arises from the inherent disconnect between the environments in which neural networks are executed (e.g., PyTorch) and verified (e.g., external tools). This discrepancy manifests in several ways: Implicit Conventions: Guarantees rely on unspoken agreements regarding operator semantics, tensor layouts, preprocessing steps, and floating-point handling. These implicit assumptions leave room for interpretation and potential errors. Observable Effect: The reliance on implicit conventions can lead to catastrophic failures in safety-critical