Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.05779.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:54:44.481451Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T14:22:35.164290Z
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 ce1dae3d-3020-48b4-9c8e-35321b41acca · inbound
From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship cites this paper.
From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:54:44.481451Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b68acfc6-2a9a-4b7e-be0e-5e362189379f · inbound
Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances cites this paper.
Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:47:44.314915Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 056595fa-a40f-47af-a7c2-a5599e3aad6b · inbound
Vision-Language Models display a strong gender bias cites this paper.
Vision-Language Models display a strong gender bias Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:05:44.821026Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b2cf18e-ff38-4505-a3d2-fc3396052351 · inbound
AHELM: A Holistic Evaluation of Audio-Language Models cites this paper.
AHELM: A Holistic Evaluation of Audio-Language Models Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T14:22:35.170240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.