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Paper Citation Record · LEDGER

Large Language Model for Vulnerability Detection: Emerging Results and Future Directions

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2401.15468.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2401.15468 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:26:43.591714Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-13T20:53:15.878558Z

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 f530851d-58c1-4531-917b-367157e47a07 · inbound

Large Language Models for In-File Vulnerability Localization Can Be "Lost in the End" cites this paper.

Large Language Models for In-File Vulnerability Localization Can Be "Lost in the End" Large Language Model for Vulnerability Detection: Emerging Results and Future Directions

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T17:26:43.591714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:26:43.591714Z digest=sha256:33921bc97c59ae06bf1c9f98e6c5ec5e9199cf58bb087e94a7337c3900178baa

Observation 9c98fb97-15f3-4415-8d59-3ceb14448b34 · inbound

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation cites this paper.

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Large Language Model for Vulnerability Detection: Emerging Results and Future Directions

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-05T16:10:53.524187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:10:53.524187Z digest=sha256:193d8080cf1692d10a8077eb4d4154ed3bfa68ecdc13dd782f73aed788aeef31

Observation d46395e7-14a0-48cb-9541-e90503b3e76a · inbound

From Theory to Practice: Code Generation Using LLMs for CAPEC and CWE Frameworks cites this paper.

From Theory to Practice: Code Generation Using LLMs for CAPEC and CWE Frameworks Large Language Model for Vulnerability Detection: Emerging Results and Future Directions

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:53:15.879940Z

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.

source=pdf_text observed=2026-05-13T20:51:00.073781Z digest=sha256:67c048165a6307911b286167f29daf62c2b5f6a2dfdcfdabd3cce925a8b17889