Paper Citation Record · LEDGER

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2507.03007.

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

pith.paper-citation-record.v1
2507.03007 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:53:11.427227Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T04:32:10.490472Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy30
unresolved1
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02e2982e-3c33-4205-8544-595327bdd45d · outbound

This paper cites Mersenne Twister: A 623-dimensionally equidistributed uniform pseudo-random number generator,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Mersenne Twister: A 623-dimensionally equidistributed uniform pseudo-random number generator,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:16.628988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76b46eea-8d5e-4195-b3a8-b8c13e9f34c6 · outbound

This paper cites Parallel random numbers: As easy as 1, 2, 3.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Parallel random numbers: As easy as 1, 2, 3

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:16.485436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 127e8156-f4ee-4cac-814f-35778d0f8693 · outbound

This paper cites PCG: A family of simple fast space-efficient statistically good algorithms for random number generation,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies PCG: A family of simple fast space-efficient statistically good algorithms for random number generation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:16.290935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4772c284-874f-46df-9bcf-d7d827e70179 · outbound

This paper cites TestU01: AC library for empirical testing of random number generators,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies TestU01: AC library for empirical testing of random number generators,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:16.141882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a30a5b1-44fe-42ad-98bb-9224d2f22cb3 · outbound

This paper cites SIMD-oriented fast Mersenne Twister: A 128-bit pseudorandom number generator,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies SIMD-oriented fast Mersenne Twister: A 128-bit pseudorandom number generator,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.899255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 24b58c92-1318-4173-8549-f5881c000a0f · outbound

This paper cites A statistical test suite for random and pseudorandom number generators for cryptographic applications,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies A statistical test suite for random and pseudorandom number generators for cryptographic applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.710346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de750279-5f21-47c1-8492-fa8a68ce495e · outbound

This paper cites Comparing search algorithms on the retrosynthesis problem,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Comparing search algorithms on the retrosynthesis problem,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.516921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cd2740ba-a96c-4496-91da-06470ece0f08 · outbound

This paper cites Replicability is not reproducibility: Nor is it good science,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Replicability is not reproducibility: Nor is it good science,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.361180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8170ff4-a011-48f1-9ede-f9e5c95dd3a0 · outbound

This paper cites Trust not verify? The critical need for data curation standards in materials informatics,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Trust not verify? The critical need for data curation standards in materials informatics,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.198789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bbc08f8-afc1-4f3a-90c4-17b1e1563a32 · outbound

This paper cites Reproducibility, replicability and repeatability: A survey of reproducible research with a focus on high performance computing,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Reproducibility, replicability and repeatability: A survey of reproducible research with a focus on high performance computing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.002094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04137055-851b-4647-a80f-42646fb1e6cf · outbound

This paper cites Random number generators in training of contextual neural networks,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Random number generators in training of contextual neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:14.842652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1e02333-fb67-4cde-88d5-23a561aa2c42 · outbound

This paper cites Quality of randomness and node dropout regularization for fitting neural networks,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Quality of randomness and node dropout regularization for fitting neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:14.657116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87b102fe-f207-4adf-a8eb-9a42c1c7d7a8 · outbound

This paper cites A general analysis of example-selection for stochastic gradient descent,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies A general analysis of example-selection for stochastic gradient descent,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:14.473435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1da8fe8f-b8ff-4f2e-af67-08455b115fc2 · outbound

This paper cites Depth uncertainty in neural networks,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Depth uncertainty in neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:14.298264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e046b5ac-1900-4bdc-9be9-f72d94598c8e · outbound

This paper cites Data augmentation: A comprehensive survey of modern approaches,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Data augmentation: A comprehensive survey of modern approaches,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:14.166565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5fe6952c-8174-4aeb-abce-9132d0131e19 · outbound

This paper cites Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.976270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 234037df-4373-4ff2-ae7a-4401a81e8823 · outbound

This paper cites Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.804953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b7e0cb22-7c47-4a13-9f8c-fa7f83bc87f0 · outbound

This paper cites A survey of stochastic computing neural networks for machine learning applications,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies A survey of stochastic computing neural networks for machine learning applications,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.632009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b603eb0-0d5f-4dfa-adac-3bd3e9e012aa · outbound

This paper cites Bayesian learning for neural networks: An algorithmic survey,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Bayesian learning for neural networks: An algorithmic survey,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.520847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f113a07c-4f06-4b6d-a604-b4b3b4812cb9 · outbound

This paper cites Recent advances in variational autoencoders with representation learning for biomedical informatics: A survey,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Recent advances in variational autoencoders with representation learning for biomedical informatics: A survey,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.353694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3890f474-4e17-4408-8c0f-f00bf2f64966 · outbound

This paper cites Exploration in deep reinforcement learning: A survey,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Exploration in deep reinforcement learning: A survey,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.198290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2cf9bbb5-b5e4-4535-ab98-762b3f8651c1 · outbound

This paper cites Noise optimization in artificial neural networks,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Noise optimization in artificial neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:13.025330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6aa7e2a3-ccec-4c59-8035-a34e1572c0d2 · outbound

This paper cites Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.849055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e337a458-ecc4-4185-8d7e-5714ef9b04f3 · outbound

This paper cites An energy efficient online learning stochastic computational deep belief network,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies An energy efficient online learning stochastic computational deep belief network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.675680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a417cbe4-e885-4d1a-afaa-ea53dc6ca4f1 · outbound

This paper cites Transformer-based generative adversarial networks in computer vision: A comprehensive survey,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Transformer-based generative adversarial networks in computer vision: A comprehensive survey,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.531100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ac49f96d-20b8-46c4-b228-680c388fd545 · outbound

This paper cites Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:53:11.609303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ce97a39-ca29-4f4f-930c-b2f3a0bb9d36 · outbound

This paper cites From local pseudorandom generators to hardness of learning,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies From local pseudorandom generators to hardness of learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.395227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14ceb6a9-4496-4661-a4aa-2454d87ae3ab · outbound

This paper cites Explainable AI models for predicting drop coalescence in microfluidics device,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Explainable AI models for predicting drop coalescence in microfluidics device,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.255112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2215dca7-4ac0-4abd-8398-5acc9fa3d73a · outbound

This paper cites Analyzing drop coalescence in microfluidic devices with a deep learning generative model,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Analyzing drop coalescence in microfluidic devices with a deep learning generative model,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.071185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a40d1c0b-4fdf-48b5-b121-4a1389e9c206 · outbound

This paper cites Sources of Irreproducibility in Machine Learning: A Review.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Sources of Irreproducibility in Machine Learning: A Review

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T20:53:11.310105Z

Source-reported events for the cited work

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Observation 672f4a28-d859-47c6-9dc4-f12ac587c5f2 · outbound

This paper cites Identifying quality Mersenne Twister streams for parallel stochastic simulations,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Identifying quality Mersenne Twister streams for parallel stochastic simulations,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:11.925783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7ffae51-1046-4fb4-ba50-8d238fd381d3 · outbound

This paper cites Scrambled linear pseudorandom number generators,.

Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies Scrambled linear pseudorandom number generators,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:11.825727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 73a58ffa-2003-4396-8b56-aae89a60dce9 · inbound

Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality cites this paper.

Pseudorandom Streams within Diffusion Models Act as Learnable Inputs That Affect Generation Quality Statistical Quality and Reproducibility of Pseudorandom Number Generators in Machine Learning technologies

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T04:32:10.490472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.