Logic Gate Neural Networks are Good for Verification
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911180560269312 |
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| author | Kresse, Fabian Yu, Emily Lampert, Christoph H. Henzinger, Thomas A. |
| author_facet | Kresse, Fabian Yu, Emily Lampert, Christoph H. Henzinger, Thomas A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Logic Gate Neural Networks are Good for Verification Kresse, Fabian Yu, Emily Lampert, Christoph H. Henzinger, Thomas A. Machine Learning Logic in Computer Science 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. |
| title | Logic Gate Neural Networks are Good for Verification |
| topic | Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2505.19932 |