---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Fabian
      foaf_name: Kresse, Fabian
      foaf_surname: Kresse
      foaf_workInfoHomepage: http://www.librecat.org/personId=faff3c84-23f6-11ef-9085-e5187b51c604
  - foaf_Person:
      foaf_givenName: Zhengqi
      foaf_name: Yu, Zhengqi
      foaf_surname: Yu
      foaf_workInfoHomepage: http://www.librecat.org/personId=20aa2ae8-f2f1-11ed-bbfa-8205053f1342
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  - foaf_Person:
      foaf_givenName: Thomas A
      foaf_name: Henzinger, Thomas A
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=40876CD8-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2985-7724
  bibo_volume: 288
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2640-3498
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: Logic gate neural networks are good for verification@
...