Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.05926.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:40:32.572712Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T20:40:32.949365Z
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 ca1477b1-6c72-4774-8258-d9ab2dd22683 · inbound
ResidualDroppath: Enhancing Feature Reuse over Residual Connections cites this paper.
ResidualDroppath: Enhancing Feature Reuse over Residual Connections SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
Reference 98
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T20:40:32.953829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2b0eeedb-b04c-4eb3-b937-e97b915835b4 · inbound
Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles cites this paper.
Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:48:16.041090Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16364baa-ecb8-4c91-9946-be05fc7fd87b · inbound
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing cites this paper.
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T08:38:02.220727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.