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Paper Citation Record · LEDGER

Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.02681.

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

pith.paper-citation-record.v1
2410.02681 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:54:03.736829Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f78fdfef-2eea-4d04-9273-1a5bcb9aaf88 · inbound

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models cites this paper.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-08T12:54:03.736829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T12:54:03.736829Z digest=sha256:dd6a14d36d76d01714096ab96c6345a5f2dbed765e09ca5e1fde143497beabc1

Observation 078e37aa-4797-450d-a799-0dcf0c899fc9 · inbound

Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift cites this paper.

Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T18:11:41.113651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:11:41.113651Z digest=sha256:137256fe05cf283ccb66b692b74562ea41958f37ab49cc83972d71b38ab67122

Observation 9634c73e-ec87-4907-9270-a19533a150a2 · inbound

Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning cites this paper.

Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:11:01.351069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:11:42.935453Z digest=sha256:171da1026d34cdeee15fe49237f32351b6800b59aa376f5ba34b12758a046613

Observation c5b120e4-d4a9-4928-8969-5da3618e2b6c · inbound

Make Your LVLM KV Cache More Lightweight cites this paper.

Make Your LVLM KV Cache More Lightweight Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

Reference 93

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T19:05:10.233691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-09T19:04:48.058450Z digest=sha256:d6c1b625486ece53e13c8350ff229d0070980cdb35203383f3975104a0094ed2

Observation 2de76ff4-7b81-4aad-aef9-dfc3257ce928 · inbound

AOEPT: Breaking the Implicit Modality-Reduction Bottleneck in Modality-Missing Prompt Tuning cites this paper.

AOEPT: Breaking the Implicit Modality-Reduction Bottleneck in Modality-Missing Prompt Tuning Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T12:24:39.658075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T12:22:17.966775Z digest=sha256:22645f890848645cee507ee270faa6bde8ad2a922a3db338718ec5a0b17cfd06