Learning Lookahead Lemmas for Neural Network Verification

Abstract:State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism. We introduce an inprocessing framework for neural network verification driven by the lookahead procedure. Under this framework, lookahead derives new lemmas over the phases of unstable ReLUs, which are collected into an implication graph that is used to prune the search space and vivify boolean cuts. We instantiate the framework in two state-of-the-art verifiers, Marabou and $\alpha$-$\beta$-CROWN, and demonstrate that it improves performance in both, proving up to 34% more instances unsatisfiable.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)

arXiv:2607.29051 [cs.LG]

(or arXiv:2607.29051v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.29051 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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