mlsanityData to 14 Oct 2026

Logic Gate Neural Networks are Good for Verification

arXiv 2505.19932 ↗First posted 26 May 2025cs.LG cs.LOcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

24 of 41 references.

References · 24

  1. 2016Concrete Problems in AI Safetycited by 1,642
  2. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  3. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  4. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 449
  5. 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 228
  6. 2019Algorithms for Verifying Deep Neural Networkscited by 221
  7. 2020Binary Neural Networks: A Surveycited by 190
  8. 2018Formal Security Analysis of Neural Networks using Symbolic Intervalscited by 185
  9. 2018Semidefinite relaxations for certifying robustness to adversarial examplescited by 179
  10. 2018Efficient Formal Safety Analysis of Neural Networkscited by 165
  11. 2018Strong mixed-integer programming formulations for trained neural networkscited by 115
  12. 2019Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programmingcited by 101
12 more
  1. 2024Marabou 2.0: A Versatile Formal Analyzer of Neural Networkscited by 81
  2. 2022Deep Differentiable Logic Gate Networkscited by 67
  3. 2020Enabling certification of verification-agnostic networks via memory-efficient semidefinite programmingcited by 66
  4. 2024Convolutional Differentiable Logic Gate Networkscited by 37
  5. 2020Efficient Exact Verification of Binarized Neural Networkscited by 33
  6. 2020Scalable Verification of Quantized Neural Networks (Technical Report)cited by 28
  7. 2022Fairify: Fairness Verification of Neural Networkscited by 22
  8. 2022CertiFair: A Framework for Certified Global Fairness of Neural Networkscited by 17
  9. 2022Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encodingcited by 15
  10. 2024Verification of Neural Networks' Global Robustnesscited by 10
  11. 2024Verifying Global Two-Safety Properties in Neural Networks with Confidencecited by 8
  12. 2022Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networkscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deploymentcited by 6
  2. 2025Scalable Interconnect Learning in Boolean Networkscited by 4
  3. 2025Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Controlcited by 1
  4. 2026Self-Organising Digital Circuitscited by 1