Incremental Neural Network Verification via Learned Conflicts

Neural network verifiers today mostly reason about each query in total isolation, throwing away everything learned even when the next query is nearly identical to the last. Elsaleh, Davis, Wu, and Katz fix that by caching the infeasible activation-phase combinations a solver discovers and reusing them across related queries via a SAT-style conflict check — a genuinely simple idea for a field that tends toward complexity. Built into the Marabou verifier and tested on robustness-radius checks, input splitting, and feature-set extraction, it delivers speedups of up to 1.9x. For anyone verifying safety-critical networks at scale, that's the difference between feasible and not.