Paper Citation Record · LEDGER

Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.02913.

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

pith.paper-citation-record.v1
2406.02913 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:34:03.868625Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T21:23:44.517430Z

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 291479c2-70a0-4ebe-9968-b50b502ea16c · inbound

On the Inherent Privacy of Zeroth Order Projected Gradient Descent cites this paper.

On the Inherent Privacy of Zeroth Order Projected Gradient Descent Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T19:34:03.868625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc1d2d4-768b-478e-8a59-28115aa394b8 · inbound

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs cites this paper.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:46.722919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97dc8962-58b7-4082-8f2e-190e1af79c3b · inbound

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation cites this paper.

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T08:48:01.084008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4384175e-9d6f-4d92-983c-252d0303a143 · inbound

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation cites this paper.

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T16:12:08.799813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcf35b58-9ccf-43b2-baf6-04d2ff77baf7 · inbound

AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments cites this paper.

AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:31:07.457710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d612a6a8-5bb3-481d-af2a-7b07cd94e12d · inbound

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered cites this paper.

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-20T21:23:44.518852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.