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Paper Citation Record · LEDGER

An Empirical Study Into What Matters for Calibrating Vision-Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.07417.

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

pith.paper-citation-record.v1
2402.07417 v2

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:05:17.723662Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T20:42:52.262773Z

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 9f369ef1-8440-464b-a744-e670db9486d7 · inbound

Noise is an Efficient Learner for Zero-Shot Vision-Language Models cites this paper.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models An Empirical Study Into What Matters for Calibrating Vision-Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-08T17:05:17.723662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:05:17.723662Z digest=sha256:6877f83266f9ef3918bd270cc65758a07bbd556312e75f84d3e2bcf883cd4e15

Observation c55fc64a-3b37-4880-890b-b3492bf25e95 · inbound

Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction cites this paper.

Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction An Empirical Study Into What Matters for Calibrating Vision-Language Models

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-04T20:42:52.269773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-04T20:42:51.951663Z digest=sha256:76f818e92cf783c8bb049efb6ad9b5d192c9b0ca32f58d32b969079e168ea9aa