Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.13461.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:06.572513Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T06:17:41.493364Z
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 647c464e-d2df-49d8-91f6-42febe3974d7 · inbound
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework cites this paper.
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:27:06.572513Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98cf7403-ed8a-4b78-8beb-113ce0daa732 · inbound
JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models cites this paper.
JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T13:23:28.099892Z
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.
Observation 01d71722-f246-4ee2-a871-fee656ec20bd · inbound
Uncovering Vulnerability of Vision-Language-Action Models under Joint-Level Physical Faults cites this paper.
Uncovering Vulnerability of Vision-Language-Action Models under Joint-Level Physical Faults Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T06:17:41.494662Z
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.