Paper Citation Record · LEDGER

VLind-Bench: Measuring Language Priors in Large Vision-Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.08702.

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

pith.paper-citation-record.v1
2406.08702 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:41:04.331687Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:20:24.287595Z

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 828886f5-cb10-4553-a4d4-2eeb8b5ed15f · inbound

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency cites this paper.

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:04.331687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95f1802f-089e-4d52-a589-a641b2e3f5bc · inbound

MLLMs are Deeply Affected by Modality Bias cites this paper.

MLLMs are Deeply Affected by Modality Bias VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:30:22.197925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42696a3c-e460-4e02-b84b-5a5fa5eeee81 · inbound

Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features cites this paper.

Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T20:58:24.757401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83f6e0c4-13b4-418b-8c0a-1db9950c9f0a · inbound

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions cites this paper.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:10:11.260463Z

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.

Observation e9da61a1-d342-4ebd-84c2-0261ba8320a7 · inbound

MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence cites this paper.

MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:45:55.249059Z

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.

Observation a09aef21-19c9-4333-922b-0817545ce66d · inbound

MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence cites this paper.

MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:20:24.291473Z

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.