Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.11121.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T14:58:53.606915Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T02:06:27.597285Z
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 28123cd2-25a0-4993-8190-875b0ae6552e · inbound
Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models cites this paper.
Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:58:53.606915Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 84a05098-a009-46c3-9031-06179ede2173 · inbound
Investigating Adversarial Robustness of Multi-modal Large Language Models cites this paper.
Investigating Adversarial Robustness of Multi-modal Large Language Models Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T02:06:27.599494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.