Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.20940.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:23.070263Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:11:19.769295Z
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 92c6c232-3abb-4638-a954-aac77ab971d3 · inbound
CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation cites this paper.
CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:12:23.070263Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d1ae9a99-c2d5-44df-90ae-707077e5ce7c · inbound
Adversarial Attacks Against Automated Fact-Checking: A Survey cites this paper.
Adversarial Attacks Against Automated Fact-Checking: A Survey Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:14:55.273426Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f5471f1-dcc3-4b89-bf0a-be0861f5f5e5 · inbound
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines cites this paper.
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:11:19.773794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.