---
OA_place: publisher
OA_type: gold
_id: '20256'
abstract:
- lang: eng
  text: We study the problem of predictive runtime monitoring of black-box dynamical
    systems with quantitative safety properties. The black-box setting stipulates
    that the exact semantics of the dynamical system and the controller are unknown,
    and that we are only able to observe the state of the controlled (aka, closed-loop)
    system at finitely many time points. We present a novel framework for predicting
    future states of the system based on the states observed in the past. The numbers
    of past states and of predicted future states are parameters provided by the user.
    Our method is based on a combination of Taylor’s expansion and the backward difference
    operator for numerical differentiation. We also derive an upper bound on the prediction
    error under the assumption that the system dynamics and the controller are smooth.
    The predicted states are then used to predict safety violations ahead in time.
    Our experiments demonstrate practical applicability of our method for complex
    black-box systems, showing that it is computationally lightweight and yet significantly
    more accurate than the state-of-the-art predictive safety monitoring techniques.
acknowledgement: "This work was supported in part by the ERC project ERC-2020-AdG
  101020093.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
- first_name: Fabian
  full_name: Kresse, Fabian
  id: faff3c84-23f6-11ef-9085-e5187b51c604
  last_name: Kresse
- first_name: Kaushik
  full_name: Mallik, Kaushik
  id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
  last_name: Mallik
  orcid: 0000-0001-9864-7475
- first_name: Zhengqi
  full_name: Yu, Zhengqi
  id: 20aa2ae8-f2f1-11ed-bbfa-8205053f1342
  last_name: Yu
- first_name: Dorde
  full_name: Zikelic, Dorde
  id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
  last_name: Zikelic
  orcid: 0000-0002-4681-1699
citation:
  ama: 'Henzinger TA, Kresse F, Mallik K, Yu E, Zikelic D. Predictive monitoring of
    black-box dynamical systems. In: <i>7th Annual Learning for Dynamics &#38; Control
    Conference</i>. Vol 283. ML Research Press; 2025:804-816.'
  apa: 'Henzinger, T. A., Kresse, F., Mallik, K., Yu, E., &#38; Zikelic, D. (2025).
    Predictive monitoring of black-box dynamical systems. In <i>7th Annual Learning
    for Dynamics &#38; Control Conference</i> (Vol. 283, pp. 804–816). Ann Arbor,
    MI, United States: ML Research Press.'
  chicago: Henzinger, Thomas A, Fabian Kresse, Kaushik Mallik, Emily Yu, and Dorde
    Zikelic. “Predictive Monitoring of Black-Box Dynamical Systems.” In <i>7th Annual
    Learning for Dynamics &#38; Control Conference</i>, 283:804–16. ML Research Press,
    2025.
  ieee: T. A. Henzinger, F. Kresse, K. Mallik, E. Yu, and D. Zikelic, “Predictive
    monitoring of black-box dynamical systems,” in <i>7th Annual Learning for Dynamics
    &#38; Control Conference</i>, Ann Arbor, MI, United States, 2025, vol. 283, pp.
    804–816.
  ista: 'Henzinger TA, Kresse F, Mallik K, Yu E, Zikelic D. 2025. Predictive monitoring
    of black-box dynamical systems. 7th Annual Learning for Dynamics &#38; Control
    Conference. L4DC: Learning for Dynamics &#38; Control, PMLR, vol. 283, 804–816.'
  mla: Henzinger, Thomas A., et al. “Predictive Monitoring of Black-Box Dynamical
    Systems.” <i>7th Annual Learning for Dynamics &#38; Control Conference</i>, vol.
    283, ML Research Press, 2025, pp. 804–16.
  short: T.A. Henzinger, F. Kresse, K. Mallik, E. Yu, D. Zikelic, in:, 7th Annual
    Learning for Dynamics &#38; Control Conference, ML Research Press, 2025, pp. 804–816.
conference:
  end_date: 2025-06-06
  location: Ann Arbor, MI, United States
  name: 'L4DC: Learning for Dynamics & Control'
  start_date: 2025-06-04
corr_author: '1'
date_created: 2025-08-31T22:01:32Z
date_published: 2025-06-01T00:00:00Z
date_updated: 2025-09-03T10:37:59Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
- _id: ChLa
ec_funded: 1
external_id:
  arxiv:
  - '2412.16564'
file:
- access_level: open_access
  checksum: d5236e561560635f5ae1d17de4903033
  content_type: application/pdf
  creator: dernst
  date_created: 2025-09-03T10:32:12Z
  date_updated: 2025-09-03T10:32:12Z
  file_id: '20283'
  file_name: 2025_L4DC_HenzingerT.pdf
  file_size: 489639
  relation: main_file
  success: 1
file_date_updated: 2025-09-03T10:32:12Z
has_accepted_license: '1'
intvolume: '       283'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 804-816
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
publication: 7th Annual Learning for Dynamics & Control Conference
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predictive monitoring of black-box dynamical systems
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 283
year: '2025'
...
---
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'
...