Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.05067.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T10:24:01.341011Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-14T23:07:42.934449Z
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 29579ae8-d7ef-49ea-84af-a86cbe23044f · inbound
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention cites this paper.
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention Improving Visual Prompt Tuning for Self-supervised Vision Transformers
Reference 82
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T23:07:42.937561Z
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 11b2a746-289e-4405-ac1f-b28b15a8686d · inbound
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Improving Visual Prompt Tuning for Self-supervised Vision Transformers
Reference 196
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T11:32:36.958276Z
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 cfbb1a37-2b04-4736-b1c8-256fa2ec8ed4 · inbound
Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation cites this paper.
Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Improving Visual Prompt Tuning for Self-supervised Vision Transformers
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:24:01.341011Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.