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Paper Citation Record · LEDGER

Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.13425.

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

pith.paper-citation-record.v1
2404.13425 v3

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 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:06:38.867893Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:17:40.219340Z

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 647e80db-2238-4247-b5ac-5712ba10c4ae · inbound

Confidence Elicitation: A New Attack Vector for Large Language Models cites this paper.

Confidence Elicitation: A New Attack Vector for Large Language Models Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-08T22:06:38.867893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T22:06:38.867893Z digest=sha256:335bd2d6ffe9dac2dc38b3ef3e9203ef491492b20194f6915086b3af83eb267d

Observation a41f7d7b-7040-44d1-9484-7601dfe22f2f · inbound

HRP: High-Rank Preheating for Superior LoRA Initialization cites this paper.

HRP: High-Rank Preheating for Superior LoRA Initialization Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-08T11:51:10.097799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:51:10.097799Z digest=sha256:86b2e1f960df6e8dd6584bbeb28a2232a147b24e32b6a36f8e2f49a9de6a46b7

Observation 6624d3db-2eca-46d0-bdb5-c55b267b4c91 · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

Reference 222

Resolution
verified exact
arxiv_id, observed 2026-05-22T15:34:57.738650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T15:32:15.293888Z digest=sha256:402c67c1a0044391be3463b970d856acacc695b52207c12e9b89d9ab60d22966

Observation d2a3af87-324d-49c7-ad01-28042fc97811 · inbound

Vision-EKIPL: External Knowledge-Infused Policy Learning for Visual Reasoning cites this paper.

Vision-EKIPL: External Knowledge-Infused Policy Learning for Visual Reasoning Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:37:15.005447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T10:34:48.849524Z digest=sha256:7b5dffc8b56eaf7f709aac72a664a9eab1714c020150ddcdd656d9def4576188

Observation 4692fe6e-3cfe-46df-bd83-ad22676538e1 · inbound

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning cites this paper.

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T05:17:40.220926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T13:20:38.336401Z digest=sha256:61894cdf4de593c16bfb752dc8bb840ffd0b7e335427057d290ec1b2fceff736