Paper Citation Record · LEDGER

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.11458.

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

pith.paper-citation-record.v1
2506.11458 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:04.870884Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy20
unresolved6
parse uncertain0
malformed identifier2
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de2ccb41-96f5-44b1-996d-e3d202cc01d1 · outbound

This paper cites Available online: https://www2.deloitte.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://www2.deloitte

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:11.127283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28c16584-3b75-4fd6-826b-dcb8bb25fc74 · outbound

This paper cites Avail- able online: https://eccc.weizmann.ac.il/report/2020/058/ [Accessed 20-09-2023].

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Avail- able online: https://eccc.weizmann.ac.il/report/2020/058/ [Accessed 20-09-2023]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:10.859112Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 53f624aa-03b0-452f-959a-361e30ca7413 · outbound

This paper cites Cryptology ePrint Archive, Paper 2016/116, 2016.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Cryptology ePrint Archive, Paper 2016/116, 2016

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:10.589922Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c4a738f4-cacb-4994-bca9-7c9de2868703 · outbound

This paper cites Cryptology ePrint Archive, Paper 2018/046, 2018.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Cryptology ePrint Archive, Paper 2018/046, 2018

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:10.349968Z

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Observation 43b7ac43-66a4-455d-9052-04a4fa788cc2 · outbound

This paper cites Differentially Private Simple Linear Regression.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Differentially Private Simple Linear Regression

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:01.724240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d17ab64f-6d48-4427-a0f7-d7440542f00c · outbound

This paper cites Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:01.833903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac040200-4c1f-43ce-9010-14864d38ae3f · outbound

This paper cites B., Mironov, I., Talwar, K., Zhang, L.: Deep Learning with Differential Privacy.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning B., Mironov, I., Talwar, K., Zhang, L.: Deep Learning with Differential Privacy

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:02.001979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 685a93c2-99d1-4458-9122-fe70b8fc80c9 · outbound

This paper cites Google AI Blog, 2022, Feb.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Google AI Blog, 2022, Feb

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:10.103557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b1b0372-53b7-468d-8cfa-0e9f9097be9d · outbound

This paper cites Founda- tions and Trends® in Theoretical Computer Science, vol.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Founda- tions and Trends® in Theoretical Computer Science, vol

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:09.869751Z

Source-reported events for the cited work

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Observation 6e599a22-a641-4fd5-b2d9-071b99e55864 · outbound

This paper cites Medium, Becoming Human: Artificial Intelligence Magazine, 2020, Oct.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Medium, Becoming Human: Artificial Intelligence Magazine, 2020, Oct

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:09.642742Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d6ee5f78-9967-46b0-995e-8aa084c72fb5 · outbound

This paper cites Wikipedia, Wikimedia Foundation, 2022, May.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Wikipedia, Wikimedia Foundation, 2022, May

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:09.427331Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 00fdf537-d9c3-4943-81b1-ef5164468e54 · outbound

This paper cites Between Pure and Approximate Differential Privacy.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Between Pure and Approximate Differential Privacy

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:02.721415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0d9a4ec-9784-41c1-938a-ee0498790ddc · outbound

This paper cites In: Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, Lecture Notes in Computer Science, vol.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning In: Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, Lecture Notes in Computer Science, vol

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:09.212146Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3012ad0b-2405-453c-af49-c68facdaa6ea · outbound

This paper cites In: Springer Tracts in Electrical and Electronics Engineer- ing.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning In: Springer Tracts in Electrical and Electronics Engineer- ing

Reference 14

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T04:09:08.975882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0cc89389-ecc8-485b-b14a-c77107e393b7 · outbound

This paper cites Fingerprinting Codes and the Price of Approximate Differential Privacy.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Fingerprinting Codes and the Price of Approximate Differential Privacy

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:09:05.623766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 33614718-d4c8-4357-b3b5-800136e691a7 · outbound

This paper cites Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:09:05.419485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e86e479b-1749-4598-b32f-40c9b6809381 · outbound

This paper cites In: 2017 IEEE 30th Computer Security Foundations Symposium (CSF), IEEE, 2017, Aug.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning In: 2017 IEEE 30th Computer Security Foundations Symposium (CSF), IEEE, 2017, Aug

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-07T04:09:03.286085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f0bb22b-5614-483e-92c3-f2a8c60fdd4a · outbound

This paper cites Differentially Private Ordinary Least Squares.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Differentially Private Ordinary Least Squares

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:09:05.175811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 79d5abc4-1167-42c8-bba9-a960e2b4dc6c · outbound

This paper cites Easy Differentially Private Linear Regression.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Easy Differentially Private Linear Regression

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:03.567716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 908423dc-8e9f-4b0a-93de-38be3ebe74fa · outbound

This paper cites TensorFlow.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning TensorFlow

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:08.752319Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5651a5b2-3363-48dd-af57-614d619a4fd9 · outbound

This paper cites Available online: https://www.risczero.com/about [Accessed 20- 09-2023].

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://www.risczero.com/about [Accessed 20- 09-2023]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:08.499481Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0f247716-ec82-48ab-be2f-02166dda1f68 · outbound

This paper cites Available online: https://www.kaggle.com/ datasets/prasad22/healthcare-dataset, 2022.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://www.kaggle.com/ datasets/prasad22/healthcare-dataset, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:08.208105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f17e177-7e64-4913-82cb-119c435abb41 · outbound

This paper cites Available online: https://l2ivresearch.substack.com/p/ tech-deep-dive-verifying-fhe-in-risc, 2024.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://l2ivresearch.substack.com/p/ tech-deep-dive-verifying-fhe-in-risc, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:07.969696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3b601973-6826-4a78-a64c-85b01bdf848d · outbound

This paper cites Available on- line: https://docs.google.com/spreadsheets/d/138M4R1- zS-OLBsl2VJeN anfTSCRCFc6EguYUVG-yA/edit#gid=1339763553 [Accessed 20-09-2023].

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available on- line: https://docs.google.com/spreadsheets/d/138M4R1- zS-OLBsl2VJeN anfTSCRCFc6EguYUVG-yA/edit#gid=1339763553 [Accessed 20-09-2023]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:07.716919Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 072552ad-6d03-4b84-ae36-8b24a01274a3 · outbound

This paper cites Available online: https://www.notebookcheck.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://www.notebookcheck

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:07.490375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0bf5cc26-01bc-4f29-9ed5-7ede94c5bdc0 · outbound

This paper cites Available online: https://openreview.net/ pdf?id=PQY2v6VtGe, 2024.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://openreview.net/ pdf?id=PQY2v6VtGe, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:07.169724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8584bd90-ee31-4970-a972-1946f2698402 · outbound

This paper cites Available online: https://github.com/emp-toolkit/ emp-zk, 2023.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Available online: https://github.com/emp-toolkit/ emp-zk, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:06.936759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5cf6afc5-37bf-4504-a01f-ac05b63f44a4 · outbound

This paper cites et al: Wolverine: Fast, Scalable, and Communication-Efficient Zero- Knowledge Proofs for Boolean and Arithmetic Circuits.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning et al: Wolverine: Fast, Scalable, and Communication-Efficient Zero- Knowledge Proofs for Boolean and Arithmetic Circuits

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:06.662309Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8b923b0d-b4e9-4df9-a5bf-d25540e7419d · outbound

This paper cites GitHub, 2024.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning GitHub, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:06.343775Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 19648e89-6cff-4827-af3e-2c0f1001a33f · outbound

This paper cites GitHub repository, GitHub, 2024.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning GitHub repository, GitHub, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:06.033100Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0e579b44-4562-4ae2-a0f8-0b1d8c755b47 · outbound

This paper cites an unresolved cited work.

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning Unresolved cited work

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:02.386072Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.