Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.09446.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T02:57:46.941141Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T02:16:26.845312Z
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 b813acf5-d0c6-4524-b03a-fbfe460883b9 · inbound
Grounding-Driven Attack: Improving Encoder-based Adversarial Transferability against Large Vision-Language Models cites this paper.
Grounding-Driven Attack: Improving Encoder-based Adversarial Transferability against Large Vision-Language Models Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:57:46.941141Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ad91002-c2e8-4cf7-879f-29230efe9357 · inbound
Investigating Adversarial Robustness of Multi-modal Large Language Models cites this paper.
Investigating Adversarial Robustness of Multi-modal Large Language Models Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T02:06:27.573666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0372334e-33ec-463c-9677-8bba24ef033d · inbound
Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models cites this paper.
Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T02:16:26.847659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.