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September 30, 2025Open Access

Logic Gate Neural Networks are Good for Verification

Key Points

LGNs support effective verification while achieving competitive predictive performance, addressing a significant challenge.
Robustness and fairness were verified using a novel SAT encoding methodology across five benchmark datasets.
The shift from traditional neural networks to Boolean logic gates enables a more accessible verification process.
This research establishes LGNs as a promising alternative for formal verification, highlighting their advantages over conventional models.

Abstract

Learning-based systems are increasingly deployed across various domains, yet the complexity of traditional neural networks poses significant challenges for formal verification. Unlike conventional neural networks, learned Logic Gate Networks (LGNs) replace multiplications with Boolean logic gates, yielding a sparse, netlist-like architecture that is inherently more amenable to symbolic verification, while still delivering promising performance. In this paper, we introduce a SAT encoding for verifying global robustness and fairness in LGNs. We evaluate our method on five benchmark datasets, including a newly constructed 5-class variant, and find that LGNs are both verification-friendly and maintain strong predictive performance.

Logic Gate Neural Networks are Good for Verification

Key Points

Abstract

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