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Paper Citation Record · LEDGER

Adapting to Distribution Shift by Visual Domain Prompt Generation

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.02797.

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

pith.paper-citation-record.v1
2405.02797 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:44:59.595503Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T23:21:07.886042Z

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 53faa9e7-e428-4590-84ae-114948821528 · inbound

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning cites this paper.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Adapting to Distribution Shift by Visual Domain Prompt Generation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T16:44:59.595503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:44:59.595503Z digest=sha256:522774278614961f7164ee7776924ff6b1bea72f817b854c30e36a1fd7b4dc11

Observation 45375b19-4352-411f-9c8d-99ce71a52ef3 · inbound

Generalizing vision-language models to novel domains: A comprehensive survey cites this paper.

Generalizing vision-language models to novel domains: A comprehensive survey Adapting to Distribution Shift by Visual Domain Prompt Generation

Reference 192

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:21:07.961173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T23:20:56.467510Z digest=sha256:e9a4a8669dbd85ad705a0a0f156b2254856a0bcb9dea9695910ab775cbda886f