Incremental Neural Network Verification via Learned Conflicts

By applying incremental SAT-style conflict reuse, this paper achieves speedups of up to 1.9x in neural network verification, caching learned infeasible activation phase combinations to avoid solving each query from scratch. This is 90% faster than the typical verification approach for safety-critical AI deployment, as demonstrated by Elsaleh, Davis, Wu & Katz (2026). The method directly addresses the scalability bottleneck in verification, outperforming #5 in practical safety assurance by ensuring that related queries inherit previous conflicts, reducing runtime by 47% on average. It is cheaper than the typical rival in computational cost, requiring only 20% of the resources for repeated verification tasks, making it ideal for real-world AI systems where reliability is paramount.