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Paper Citation Record · LEDGER

Digital Forensics in the Age of Large Language Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2504.02963.

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

pith.paper-citation-record.v1
2504.02963 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:09:01.393974Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:26:24.829911Z

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 4eaaa9b9-46bd-4c3d-98e5-f2b8a6a11824 · inbound

DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response cites this paper.

DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response Digital Forensics in the Age of Large Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T14:09:01.393974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:01.393974Z digest=sha256:9bb06d85ebccb0bfdbb353b1862ec235a70158e13b70ebbce50f770020c80528

Observation 6af03889-af3b-411f-b8cd-75242a7d8fa7 · inbound

AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models cites this paper.

AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models Digital Forensics in the Age of Large Language Models

Reference 77

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:26:24.833467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-05T13:26:24.602487Z digest=sha256:2600969cea7d024ab98478c4af9dedd89aa0d98415ac48a79099a4e4102afcfa