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Paper Citation Record · LEDGER

LawLLM: Law Large Language Model for the US Legal System

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.21065.

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

pith.paper-citation-record.v1
2407.21065 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:08:50.905125Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:07:25.017824Z

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 854e0dd5-4ff6-47ca-9d3f-a6f116a7294d · inbound

Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders cites this paper.

Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders LawLLM: Law Large Language Model for the US Legal System

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T19:08:50.905125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:08:50.905125Z digest=sha256:928ebcf10d52b22fc80c80dbebd7ca135e31a0e8c62204f03c72e7b02e1e6c7b

Observation fff2e99a-c35c-49a0-bf7e-3ff7dae062ef · inbound

LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa cites this paper.

LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa LawLLM: Law Large Language Model for the US Legal System

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:07:25.020672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:07:23.985055Z digest=sha256:6023048d24c2bf26132bddabf8bb9fe4a68d0cccffecbb538bffa65814d45cd8