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Paper Citation Record · LEDGER

Adversarial Robustness for Visual Grounding of Multimodal Large Language Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.09981.

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

pith.paper-citation-record.v1
2405.09981 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:45:19.333956Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T12:56:14.818702Z

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 212eeb3e-c74d-4a63-9d36-5f9e29e73712 · inbound

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations cites this paper.

A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations Adversarial Robustness for Visual Grounding of Multimodal Large Language Models

Reference 96

Resolution
unresolved
no resolver link, observed 2026-08-07T19:45:19.333956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:45:19.333956Z digest=sha256:253dc1dc8b9f93303a98c2be65f2f13ee5b9c54cc095d5729d38d6d42a8f329b

Observation ee463007-42e4-4216-9e93-2c98af29fea4 · inbound

Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations cites this paper.

Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations Adversarial Robustness for Visual Grounding of Multimodal Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T21:07:15.083166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:07:15.083166Z digest=sha256:f28efc7ace4376465fd74753143cfa63406c951f08aeeeb5eb91b441166b85dc

Observation d336c808-85cf-4793-9097-fce9154a614d · inbound

Pay Less Attention to Function Words for Free Robustness of Vision-Language Models cites this paper.

Pay Less Attention to Function Words for Free Robustness of Vision-Language Models Adversarial Robustness for Visual Grounding of Multimodal Large Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-17T00:33:44.714084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-17T00:33:13.977360Z digest=sha256:656f4366218b52831771f9fe404646a8e41c1eb9f8e448efdec931195fa43ea0

Observation 01726793-2cec-4009-9f34-2f9acb5e0aba · inbound

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces cites this paper.

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces Adversarial Robustness for Visual Grounding of Multimodal Large Language Models

Reference 19

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T12:56:14.820422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-09T12:55:34.631248Z digest=sha256:38e66688dcf771272db37cc29f1f334305f383765358206b47cd19c16139b14e