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Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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:fa26d849f1ed4c79d43ee2a73cd66d74a8b3b5461280a295e349b551bbeaf73d

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=sha256:4fafc0de515cdc179965ee8a1e31528c5f5f97a24591f8fb617ef7482d114e6f

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T15:32:15.293888Z digest=sha256:150a9cb4af9c31840bf29add68a5f118d310eff348cecd1203796d83100d35ca

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T10:34:48.849524Z digest=sha256:627e516effb0d3fb3c373106ef2c0288b54335708280881b35435d5b8da8791e

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T13:20:38.336401Z digest=sha256:7d477691a0a5a06188024ef1df63ed879dc09e31a09bcf0dfccc3103ae518aa4