🤖 AI Summary
This work addresses the challenge of maintaining consistency between safety operation rules and actual system behavior in cyber-physical systems, where rules must be continuously refined as environments evolve. Such refinement must adhere to domain-specific syntactic constraints while avoiding semantic inconsistencies or overfitting. The paper proposes a language-model-in-the-loop optimization framework that integrates counterfactual reasoning with syntactic constraints to iteratively refine safety rules. Evaluated in an autonomous driving control system, the approach successfully corrects inconsistent rules generated by conventional methods, ensuring both syntactic validity and behavioral alignment. Large-scale experiments further demonstrate that the choice of large language model critically influences correction quality, offering a novel paradigm for high-assurance safety verification.
📝 Abstract
Safety specifications in cyber-physical systems (CPS) capture the operational conditions the system must satisfy to operate safely within its intended environment. As operating environments evolve, operational rules must be continuously refined to preserve consistency with observed system behavior during simulation-based verification and validation. Revising inconsistent rules is challenging because the changes must remain syntactically correct under a domain-specific grammar. Language-in-the-loop refinement further raises safety concerns beyond syntactic violations, as it can produce semantically unjustified refinements that overfit to the observed outcomes. We introduce a framework that combines counterfactual reasoning with a grammar-constrained refinement loop to refine operational rules, aligning them with the observed system behavior. Applied to an autonomous driving control system, our approach successfully resolved the inconsistencies in an operational rule inferred by a conventional baseline while remaining grammar compliant. An empirical large language model (LLM) study further revealed model-dependent refinement quality and safety lessons, which motivate rigorous grammar enforcement, stronger semantic validation, and broader evaluation in future work.