Cycle-Consistent Neural Explanation of Formal Verification Certificates

The paper introduces a cycle-consistent neural architecture designed to generate natural language explanations for formal verification certificates, comprising a forward network (NN1) and an inverse network (NN2) that together ensure faithful reconstruction of the certificates. Evaluated on 420 test certificates from various verification methods, the model achieves 90.0% cycle-verified soundness, significantly outperforming a multi-LLM few-shot baseline by 13.9 percentage points, while also providing 860x faster inference times and deterministic outputs. This advancement is crucial for practitioners as it enables efficient, offline explanations of verification results, enhancing accessibility for non-specialists without the reliance on cloud-based systems.