Paper Citation Record · LEDGER

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems

As of 12 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2507.01078.

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

pith.paper-citation-record.v1
2507.01078 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:09:23.728876Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:42:10.331409Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy11
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45f9929d-0e8d-47f8-a891-407be640d4d8 · outbound

This paper cites Reproducibility in Machine Learning-Driven Research.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Reproducibility in Machine Learning-Driven Research

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T21:09:23.689619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d623397c-557a-4f88-92b4-ab869ca0ebe4 · outbound

This paper cites Challenges for the repeatability of deep learning models,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Challenges for the repeatability of deep learning models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.857268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 621361c2-b3ad-4aea-88a5-0294c3e55871 · outbound

This paper cites Out-of-the-box reproducibility: A sur- vey of machine learning platforms,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Out-of-the-box reproducibility: A sur- vey of machine learning platforms,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.848325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2cbc5dd2-b709-49cd-803c-3622857a71d9 · outbound

This paper cites Deephyper: Asynchronous hyperparameter search for deep neural networks,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Deephyper: Asynchronous hyperparameter search for deep neural networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.839709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 716b2c91-e17c-44e0-949e-b4a4d2118148 · outbound

This paper cites Provenance: a future history,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Provenance: a future history,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.831141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 833dc1d2-8eac-41e4-858f-d7db265f714e · outbound

This paper cites Towards lightweight data integration using multi-workflow provenance and data observability,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Towards lightweight data integration using multi-workflow provenance and data observability,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.820200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c8bf9f07-a23d-463b-b5ca-94e33ddc17ac · outbound

This paper cites Workflow provenance in the computing continuum for responsible, trustworthy, and energy-efficient ai,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Workflow provenance in the computing continuum for responsible, trustworthy, and energy-efficient ai,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.810775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eba9ce22-96e7-434c-8e85-ba00d4349b1c · outbound

This paper cites Accelerating the machine learning lifecycle with mlflow,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Accelerating the machine learning lifecycle with mlflow,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.800751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9bdebac3-a2d8-4e67-ad45-28d95da9a41f · outbound

This paper cites Interoperability for provenance-aware databases using PROV and JSON,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Interoperability for provenance-aware databases using PROV and JSON,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.791531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eac73008-1141-4236-b22f-95a63fa87ce5 · outbound

This paper cites A graph data model-based micro-provenance approach for multi-level provenance exploration in end-to-end climate workflows,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems A graph data model-based micro-provenance approach for multi-level provenance exploration in end-to-end climate workflows,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.781338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5afa1dd3-be27-4c80-8599-d0e89361e967 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems The mnist database of handwritten digit images for machine learning research,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:09:23.721318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5d2ce9c-ce59-4688-b730-7037073c6968 · outbound

This paper cites Exploring vision transformers on the frontier supercomputer for remote sensing and geoscientific applications,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems Exploring vision transformers on the frontier supercomputer for remote sensing and geoscientific applications,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.768932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8d31f4cc-96fa-41ca-ab36-a4cc37e00679 · outbound

This paper cites A software ecosystem for multi-level provenance man- agement in large-scale scientific workflows for ai applications,.

yProv4ML: Effortless Provenance Tracking for Machine Learning Systems A software ecosystem for multi-level provenance man- agement in large-scale scientific workflows for ai applications,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:09:23.759632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation e37d2062-63e1-4df8-bff4-75e840325ed0 · inbound

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets cites this paper.

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets yProv4ML: Effortless Provenance Tracking for Machine Learning Systems

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T13:42:10.331409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.