AI Explains Formal Verification Certificates for Non-Specialists

Formal verification processes generate highly technical certificates that confirm or deny the satisfaction of temporal properties in systems. These certificates are typically machine-readable but opaque to individuals without specialized knowledge. A new cycle-consistent neural architecture has been introduced to bridge this gap by translating these complex certificates into clear, natural language explanations. The architecture employs a forward network to map certificates to explanations and an inverse network to reconstruct certificates from these explanations, with a symbolic verifier ensuring faithfulness. It leverages a pointer-generator mechanism to maintain lexical grounding by directly copying state names. Tested on a financial compliance domain, the system achieved 90.0% cycle-verified soundness, significantly outperforming multi-LLM few-shot baselines, and offered substantially faster, offline, and deterministic inference.