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This paper conducts a systematic study on template-based acceleration for neural network robustness verification and introduces FastCert, a technique to automatically distribute templates for improved performance.
This paper presents three composable methods—abstract interpretation, refinement types, and SMT-bounded model checking—to mechanically verify that an LLM-driven agent skill's behavior is contained within its declared capabilities, closing the gap to the formal verification level proposed in a companion paper.