Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.11750.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:07.181962Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T14:45:49.503478Z
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 9a29e326-eb5a-477c-bdca-687d5b6808e3 · inbound
Benign-to-Toxic Jailbreaking: Inducing Harmful Responses from Harmless Prompts cites this paper.
Benign-to-Toxic Jailbreaking: Inducing Harmful Responses from Harmless Prompts Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:01:07.181962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9af8d32-691e-4519-a28e-c37a3050dd07 · inbound
Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking cites this paper.
Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:41:02.231593Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 82879878-6e36-4883-aa64-6623b0b39d41 · inbound
WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections cites this paper.
WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
Reference 27
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T14:45:49.508234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.