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Paper Citation Record · LEDGER

Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.02507.

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

pith.paper-citation-record.v1
2410.02507 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 2026-08-05T22:28:11.667139Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:51:09.230185Z

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 8ec8f5ff-5c58-4b2b-bad5-234f8c96c2a9 · inbound

Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV cites this paper.

Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-05T22:28:11.667139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:28:11.667139Z digest=sha256:a5991299d1849c9e954168cf4dcf6793c9eb95195d21d6c59e962e1f9b1a7117

Observation ea363d11-bd5e-432e-b086-b5b34f3912f1 · inbound

Memory in the Age of AI Agents cites this paper.

Memory in the Age of AI Agents Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration

Reference 179

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T18:18:20.571765Z

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=arxiv_source observed=2026-05-11T18:18:19.911342Z digest=sha256:c5c4234734997fee6f65505290c65c4213276e395e3dce3e655796d4f6b454fd

Observation 205f962a-e3e0-43cd-a4ee-52dd66b5a171 · inbound

A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework cites this paper.

A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration

Reference 142

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:51:09.234878Z

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=2026-05-08T07:53:13.746141Z digest=sha256:57d121f9d675b4a52691a2c7d1251975e939b563b1921f32d1ad951e154d681b