---
OA_place: publisher
OA_type: diamond
_id: '20296'
abstract:
- lang: eng
  text: 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.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This work is supported in part by the ERC grant under Grant No.
  ERC-2020-AdG 101020093 and\r\nthe Austrian Science Fund (FWF) [10.55776/COE12].
  This research was supported by the Scientific\r\nService Units (SSU) of ISTA through
  resources provided by Scientific Computing (SciComp)."
alternative_title:
- PMLR
article_number: '26'
article_processing_charge: No
arxiv: 1
author:
- first_name: Fabian
  full_name: Kresse, Fabian
  id: faff3c84-23f6-11ef-9085-e5187b51c604
  last_name: Kresse
- first_name: Zhengqi
  full_name: Yu, Zhengqi
  id: 20aa2ae8-f2f1-11ed-bbfa-8205053f1342
  last_name: Yu
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
citation:
  ama: 'Kresse F, Yu E, Lampert C, Henzinger TA. Logic gate neural networks are good
    for verification. In: <i>2nd International Conferenceon Neuro-Symbolic Systems</i>.
    Vol 288. ML Research Press; 2025.'
  apa: 'Kresse, F., Yu, E., Lampert, C., &#38; Henzinger, T. A. (2025). Logic gate
    neural networks are good for verification. In <i>2nd International Conferenceon
    Neuro-Symbolic Systems</i> (Vol. 288). Philadephia, PA, United States: ML Research
    Press.'
  chicago: Kresse, Fabian, Emily Yu, Christoph Lampert, and Thomas A Henzinger. “Logic
    Gate Neural Networks Are Good for Verification.” In <i>2nd International Conferenceon
    Neuro-Symbolic Systems</i>, Vol. 288. ML Research Press, 2025.
  ieee: F. Kresse, E. Yu, C. Lampert, and T. A. Henzinger, “Logic gate neural networks
    are good for verification,” in <i>2nd International Conferenceon Neuro-Symbolic
    Systems</i>, Philadephia, PA, United States, 2025, vol. 288.
  ista: 'Kresse F, Yu E, Lampert C, Henzinger TA. 2025. Logic gate neural networks
    are good for verification. 2nd International Conferenceon Neuro-Symbolic Systems.
    NeuS: International Conferenceon Neuro-Symbolic Systems, PMLR, vol. 288, 26.'
  mla: Kresse, Fabian, et al. “Logic Gate Neural Networks Are Good for Verification.”
    <i>2nd International Conferenceon Neuro-Symbolic Systems</i>, vol. 288, 26, ML
    Research Press, 2025.
  short: F. Kresse, E. Yu, C. Lampert, T.A. Henzinger, in:, 2nd International Conferenceon
    Neuro-Symbolic Systems, ML Research Press, 2025.
conference:
  end_date: 2025-05-30
  location: Philadephia, PA, United States
  name: 'NeuS: International Conferenceon Neuro-Symbolic Systems'
  start_date: 2025-05-28
corr_author: '1'
date_created: 2025-09-07T22:01:34Z
date_published: 2025-06-01T00:00:00Z
date_updated: 2025-09-09T08:12:44Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
- _id: ToHe
ec_funded: 1
external_id:
  arxiv:
  - '2505.19932'
file:
- access_level: open_access
  checksum: 90a32defed34787e771a5c1623b6b0d2
  content_type: application/pdf
  creator: dernst
  date_created: 2025-09-09T08:10:13Z
  date_updated: 2025-09-09T08:10:13Z
  file_id: '20314'
  file_name: 2025_NeuS_Kresse.pdf
  file_size: 295466
  relation: main_file
  success: 1
file_date_updated: 2025-09-09T08:10:13Z
has_accepted_license: '1'
intvolume: '       288'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
publication: 2nd International Conferenceon Neuro-Symbolic Systems
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Logic gate neural networks are good for verification
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 288
year: '2025'
...