Paper Citation Record · LEDGER

Improving Visual Prompt Tuning for Self-supervised Vision Transformers

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.05067.

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

pith.paper-citation-record.v1
2306.05067 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:57:09.990767Z

measured 0 of 1 external citation measurements

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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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.

Observation 4cdef414-dd38-4e57-8708-5ba639040313 · inbound

Token Coordinated Prompt Attention is Needed for Visual Prompting cites this paper.

Token Coordinated Prompt Attention is Needed for Visual Prompting Improving Visual Prompt Tuning for Self-supervised Vision Transformers

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T00:57:09.990767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.