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Paper Citation Record · LEDGER

Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.01743.

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

pith.paper-citation-record.v1
2501.01743 v3

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:26:14.210162Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T21:42:48.160923Z

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 7822ab61-3f1d-4119-9665-a80434b47f39 · inbound

Large Language Models Meet Legal Artificial Intelligence: A Survey cites this paper.

Large Language Models Meet Legal Artificial Intelligence: A Survey Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-04T18:26:14.210162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:26:14.210162Z digest=sha256:fa8d4d7aee68b4c04d6ae615aa1f784ff9bf066c2b6918f48479e7134f9605fa

Observation e448808f-2b4f-4424-be75-379de17069cf · inbound

Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free cites this paper.

Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-19T21:42:48.165690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T21:40:29.026439Z digest=sha256:b927b5644a70a2a010810eedc9229d9c5674c389ca06d80ce53f0eaef6f695d8