Paper Citation Record · LEDGER

Mining Verdict Boundaries for Neural Network Verification

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.28954.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.28954 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:43:41.838006Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 9969511b-c526-462b-b231-33df52a16b2b · outbound

This paper cites Information processing letters5(SLAC-PUB-1679) (1976).https://doi.org/10.

Mining Verdict Boundaries for Neural Network Verification Information processing letters5(SLAC-PUB-1679) (1976).https://doi.org/10

Reference 1

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Mining Verdict Boundaries for Neural Network Verification Unresolved cited work

Reference 2

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This paper cites The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results.

Mining Verdict Boundaries for Neural Network Verification The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results

Reference 3

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This paper cites In: Con- ference on Uncertainty in Artificial Intelligence.

Mining Verdict Boundaries for Neural Network Verification In: Con- ference on Uncertainty in Artificial Intelligence

Reference 4

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This paper cites Journal of Machine Learning Research21(2020) (2020),https://jmlr.org/papers/v21/19-468.html.

Mining Verdict Boundaries for Neural Network Verification Journal of Machine Learning Research21(2020) (2020),https://jmlr.org/papers/v21/19-468.html

Reference 5

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Observation 4453c05d-0d49-48d1-926c-b685a61c7de6 · outbound

This paper cites Tighter Abstract Queries in Neural Network Verification.

Mining Verdict Boundaries for Neural Network Verification Tighter Abstract Queries in Neural Network Verification

Reference 6

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Observation bdcb44d3-b297-40a1-a733-c23aaad95b29 · outbound

This paper cites Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems.

Mining Verdict Boundaries for Neural Network Verification Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems

Reference 7

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This paper cites Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition.

Mining Verdict Boundaries for Neural Network Verification Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition

Reference 8

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This paper cites In: Automated Technology for Verification and Analysis: 15th Int.

Mining Verdict Boundaries for Neural Network Verification In: Automated Technology for Verification and Analysis: 15th Int

Reference 9

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This paper cites In: 2025 Design, Automation & Test in Europe Conference (DATE).

Mining Verdict Boundaries for Neural Network Verification In: 2025 Design, Automation & Test in Europe Conference (DATE)

Reference 10

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This paper cites In: International Conference on Machine Learning.

Mining Verdict Boundaries for Neural Network Verification In: International Conference on Machine Learning

Reference 11

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Observation b267c210-7b62-463a-b78e-55720587ca10 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Mining Verdict Boundaries for Neural Network Verification Explaining and Harnessing Adversarial Examples

Reference 12

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Observation 8e693e9b-3ea0-4d62-8298-f7ceb9f813b9 · outbound

This paper cites In: 2021 IEEE 32nd Int.

Mining Verdict Boundaries for Neural Network Verification In: 2021 IEEE 32nd Int

Reference 13

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This paper cites In: IJCAI.

Mining Verdict Boundaries for Neural Network Verification In: IJCAI

Reference 14

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Observation dbf177b0-45eb-4648-8543-f03797a51e69 · outbound

This paper cites Outside the Box: Abstraction-Based Monitoring of Neural Networks.

Mining Verdict Boundaries for Neural Network Verification Outside the Box: Abstraction-Based Monitoring of Neural Networks

Reference 15

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This paper cites In: Proceedings of the AAAIConferenceonArtificialIntelligence.vol.38,pp.21152–21160(2024).https: //doi.org/10.1609/aaai.v38i19.30108.

Mining Verdict Boundaries for Neural Network Verification In: Proceedings of the AAAIConferenceonArtificialIntelligence.vol.38,pp.21152–21160(2024).https: //doi.org/10.1609/aaai.v38i19.30108

Reference 16

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Observation 30edf357-5df2-4725-a0d7-3b8dc8946572 · outbound

This paper cites In: Computer Aided Verification: 29th Int.

Mining Verdict Boundaries for Neural Network Verification In: Computer Aided Verification: 29th Int

Reference 17

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Observation 42619251-e478-442c-8327-54219bab97e7 · outbound

This paper cites DNN Verification, Reachability, and the Exponential Function Problem.

Mining Verdict Boundaries for Neural Network Verification DNN Verification, Reachability, and the Exponential Function Problem

Reference 18

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Observation 5e4e4012-64e7-4373-bbe0-26a95f2da371 · outbound

This paper cites In: Majumdar, R., Kunčak, V.

Mining Verdict Boundaries for Neural Network Verification In: Majumdar, R., Kunčak, V

Reference 19

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This paper cites Foundations and Trends®in Op- timization4(3-4), 244–404 (2021).https://doi.org/10.1561/2400000035.

Mining Verdict Boundaries for Neural Network Verification Foundations and Trends®in Op- timization4(3-4), 244–404 (2021).https://doi.org/10.1561/2400000035

Reference 20

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This paper cites In: International Conference on Runtime Verification.

Mining Verdict Boundaries for Neural Network Verification In: International Conference on Runtime Verification

Reference 21

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Observation 13447ed4-b21e-4729-a7e3-b8b771194c76 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Mining Verdict Boundaries for Neural Network Verification Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 22

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This paper cites Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates.

Mining Verdict Boundaries for Neural Network Verification Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates

Reference 23

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Observation 58e0b8a7-806c-4681-89c8-48774b2702a7 · outbound

This paper cites In: Principles of Verification: Cycling the Probabilis- tic Landscape: Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part III, pp.

Mining Verdict Boundaries for Neural Network Verification In: Principles of Verification: Cycling the Probabilis- tic Landscape: Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part III, pp

Reference 24

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Mining Verdict Boundaries for Neural Network Verification In: International Symposium on Auto- mated Technology for Verification and Analysis

Reference 25

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This paper cites mining verdict boundaries for neural network verification.

Mining Verdict Boundaries for Neural Network Verification mining verdict boundaries for neural network verification

Reference 26

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This paper cites Advances in Neural Information Processing Systems35, 2350– 2364 (2022),http://papers.nips.cc/paper_files/paper/2022/hash/ 0ff54b4ec4f70b3ae12c8621ca8a49f4-Abstract-Conference.html.

Mining Verdict Boundaries for Neural Network Verification Advances in Neural Information Processing Systems35, 2350– 2364 (2022),http://papers.nips.cc/paper_files/paper/2022/hash/ 0ff54b4ec4f70b3ae12c8621ca8a49f4-Abstract-Conference.html

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Mining Verdict Boundaries for Neural Network Verification In: Advances in Neural Information Processing 20 J

Reference 28

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This paper cites ACM on Programming Languages3(POPL), 1–30 (2019).https: //doi.org/10.1145/3290354.

Mining Verdict Boundaries for Neural Network Verification ACM on Programming Languages3(POPL), 1–30 (2019).https: //doi.org/10.1145/3290354

Reference 29

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Mining Verdict Boundaries for Neural Network Verification Machine Learning112(10), 3685–3712 (2023).https://doi.org/10.1007/ s10994-021-06050-2

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Mining Verdict Boundaries for Neural Network Verification Advances in neural information process- ing systems31(2018),https://proceedings.neurips.cc/paper/2018/hash/ 2ecd2bd94734e5dd392d8678bc64cdab-Abstract.html

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Mining Verdict Boundaries for Neural Network Verification In: 27th USENIX Security Symp

Reference 33

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Mining Verdict Boundaries for Neural Network Verification Advances in Neural Information Processing Sys- tems34, 29909–29921 (2021),https://proceedings.neurips.cc/paper/2021/ hash/fac7fead96dafceaf80c1daffeae82a4-Abstract.html

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Mining Verdict Boundaries for Neural Network Verification In: International Conference on Artificial Intelligence and Statistics

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Mining Verdict Boundaries for Neural Network Verification In: Gurfinkel, A., Ganesh, V

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Mining Verdict Boundaries for Neural Network Verification In: 2023 IEEE Conference on Secure and Trustworthy Machine Learn- ing (SaTML)

Reference 37

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Mining Verdict Boundaries for Neural Network Verification Advances in Neural Information Processing Systems36(2024),http://papers.nips.cc/paper_files/paper/2023/hash/ 46907c2ff9fafd618095161d76461842-Abstract-Conference.html

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Mining Verdict Boundaries for Neural Network Verification In: Raedt, L.D

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Mining Verdict Boundaries for Neural Network Verification In: 2022 Formal Methods in Computer-Aided Design (FMCAD)

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Mining Verdict Boundaries for Neural Network Verification In: 39th European Conference on Object-Oriented Program- ming (ECOOP 2025)

Reference 41

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Mining Verdict Boundaries for Neural Network Verification In: ECAI 2020, pp

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Mining Verdict Boundaries for Neural Network Verification In: Gurfinkel, A., Ganesh, V

Reference 43

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Mining Verdict Boundaries for Neural Network Verification In: Formal Methods: 26th International Sym- posium, FM 2024, Milan, Italy, September 9–13, 2024, Proceedings, Part I

Reference 44

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Pith citing papers

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