Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2304.01917.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:53:06.338788Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T23:45:53.946086Z
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 18bc4fae-ee33-413d-a9e3-744ad957b18a · inbound
F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics cites this paper.
F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:53:06.338788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 074ffc3c-4f0a-4c5a-8cbd-4a11b4338ec2 · inbound
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning cites this paper.
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:20:30.265408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7a0c1850-881c-4977-b390-7cf873c7f60c · inbound
Visual prompting reimagined: The power of the Activation Prompts cites this paper.
Visual prompting reimagined: The power of the Activation Prompts Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:45:53.953333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.