Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.13294.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T19:45:19.390062Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T19:45:21.519045Z
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 ee06b913-3201-4433-9dd0-d754a41652d4 · 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 Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens
Reference 98
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T19:45:21.525238Z
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.
Observation 122dfec8-1f73-4e3d-af47-8ae25650d400 · inbound
GhostPrompt: Cross-Image Adversarial Prompt for Vision-Language Models cites this paper.
GhostPrompt: Cross-Image Adversarial Prompt for Vision-Language Models Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T12:04:27.370700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.