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Paper Citation Record · LEDGER

Democratizing Fine-grained Visual Recognition with Large Language Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.13837.

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

pith.paper-citation-record.v1
2401.13837 v2

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 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:40:53.490479Z

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

1
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 488b98b0-3464-4cb1-a13d-b03615a1e047 · inbound

Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models cites this paper.

Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models Democratizing Fine-grained Visual Recognition with Large Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T14:40:53.490479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:40:53.490479Z digest=sha256:5fb31dace809fb24c13961ea5654bcf65eb46e73cdafda0841f7f3c9b980a591

Observation e1277d57-26d0-4621-9aa4-97a9d555b53f · inbound

An analysis of vision-language models for fabric retrieval cites this paper.

An analysis of vision-language models for fabric retrieval Democratizing Fine-grained Visual Recognition with Large Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:49.371699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:49.371699Z digest=sha256:54e03b1468295b86f2cdb649a5cbdd1469c00b8924811dd2dc979307bf55a5a5

Observation ec1c2b0c-570f-417f-8122-21e4f8930b80 · inbound

Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models cites this paper.

Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models Democratizing Fine-grained Visual Recognition with Large Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T18:27:47.128776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:27:47.128776Z digest=sha256:f8c946fa0d5f07c2c7d7c59c9f6c55018f9163094a30e076ffc6dad2094da5ea

Observation 1f1f2f89-d788-450f-b4fd-c6de4e25556b · inbound

Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification? cites this paper.

Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification? Democratizing Fine-grained Visual Recognition with Large Language Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-16T14:53:00.573567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-16T14:51:26.368439Z digest=sha256:684567556642e7585e1a6db481693d877b196131c6dbdc2791221ff4545bcce7

Observation c7079e7d-61a9-424b-b963-426d05bd6b97 · inbound

TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations cites this paper.

TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations Democratizing Fine-grained Visual Recognition with Large Language Models

Reference 120

Resolution
verified exact
arxiv_id, observed 2026-05-08T19:09:02.694967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-08T19:04:40.817413Z digest=sha256:87395d650670064c81588fc45d19a13d31287a18a2c4ea25f5cd54c80d4f000c