Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.07818.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:47:30.826841Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T21:23:44.511287Z
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 e950c7b2-765f-4696-b44f-04dc8376a704 · inbound
Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States cites this paper.
Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T12:02:16.839782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a1e3d07f-04ec-498d-9942-b5481f5b2e23 · inbound
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models cites this paper.
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:47:30.826841Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 83d1d34f-e2a2-46f3-b0da-99e118f964c0 · 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 Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
Reference 89
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T21:23:44.512805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.