Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.20796.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:37.378395Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T00:32:37.678780Z
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 ffd5f1c3-ad7b-4e55-aaad-81576e95d855 · inbound
Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability cites this paper.
Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T00:32:37.683927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 692958b1-aeb4-41f3-b15e-49334dd31303 · inbound
Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows cites this paper.
Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:06:18.864784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f6df651-9dc5-4998-b1e9-4f5cb2166c14 · inbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations cites this paper.
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T00:34:16.798511Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.