ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Models

תקציר מקורי באנגליתarXiv:2609.34457v2 Announce Type: replace Abstract: Transformer-based models are widely used for reasoning, coding, and multimodal agentic tasks. To provide formal assurance of desirable behaviors, such as robustness, safety, and fairness, neural network verification techniques prove required properties and provide auditable guarantees before deployment. However, prior work remains limited to small or restricted Transformers, and maintaining precision across deep models remains challenging. In this work, we introduce ZonoGpt, an abstract domain for verifying large transformers that maintains a space complexity independent of network depth. ZonoGpt uses a structured zonotope and a generator reduction mechanism to efficiently preserve correlations. To maintain precision, it introduces block-