Paper Citation Record · LEDGER

Optimizing ML Training with Metagradient Descent

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.13751.

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

pith.paper-citation-record.v1
2503.13751 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:23.565876Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T08:17:45.772573Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b56acc0a-63b6-4bac-9816-13317182492c · inbound

Rescaled Influence Functions: Accurate Data Attribution in High Dimension cites this paper.

Rescaled Influence Functions: Accurate Data Attribution in High Dimension Optimizing ML Training with Metagradient Descent

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:23.565876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa85e7e3-0c7f-4065-9d18-86d5f756d2d2 · inbound

Ambient Diffusion Omni: Training Good Models with Bad Data cites this paper.

Ambient Diffusion Omni: Training Good Models with Bad Data Optimizing ML Training with Metagradient Descent

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:13.570335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d598a2ec-91af-4bee-9bc0-e66be976e966 · inbound

On the Accuracy of Newton Step and Influence Function Data Attributions cites this paper.

On the Accuracy of Newton Step and Influence Function Data Attributions Optimizing ML Training with Metagradient Descent

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:50:26.331937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8a621c9-dc4e-48df-8b4c-9552b452d0e0 · inbound

Efficient Estimation of Kernel Surrogate Models for Task Attribution cites this paper.

Efficient Estimation of Kernel Surrogate Models for Task Attribution Optimizing ML Training with Metagradient Descent

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:00:44.852492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 728f42ec-647a-4e0c-915e-39c5d955d83b · inbound

How to sketch a learning algorithm cites this paper.

How to sketch a learning algorithm Optimizing ML Training with Metagradient Descent

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:30:56.486611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 334f4691-5898-4fcb-ab71-673c33427254 · inbound

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates cites this paper.

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates Optimizing ML Training with Metagradient Descent

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T11:01:04.333596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bafecd07-4277-4272-bf18-b27931587a08 · inbound

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training cites this paper.

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training Optimizing ML Training with Metagradient Descent

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:13.878540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 062b9e3b-bdcb-4adb-ae86-03b18228c5e8 · inbound

Bergson: An Open Source Library for Data Attribution cites this paper.

Bergson: An Open Source Library for Data Attribution Optimizing ML Training with Metagradient Descent

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:17:45.773887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation afb53d47-211d-446b-8a47-8809ce7e5e90 · inbound

(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure cites this paper.

(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure Optimizing ML Training with Metagradient Descent

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T17:46:56.130355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.