Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2502.17638.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:39:06.205155Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation a90de854-08a4-4ae0-bae7-946dd7566361 · inbound
The Consistency-Acceptability Divergence of LLMs in Judicial Decision-Making: Task and Stakeholder Dimensions cites this paper.
The Consistency-Acceptability Divergence of LLMs in Judicial Decision-Making: Task and Stakeholder Dimensions Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:39:06.205155Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d89fad8-5f31-45ad-8f23-1e6119d527d7 · inbound
Autonomous Business System via Neuro-symbolic AI cites this paper.
Autonomous Business System via Neuro-symbolic AI Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T12:47:53.370480Z
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.
Observation 68eb8530-42a2-4702-9404-28e821aac688 · inbound
LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw using LLMs cites this paper.
LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw using LLMs Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T18:37:26.607019Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed4753f2-3861-41e4-9375-767efbb6bb69 · inbound
GDPR Auto-Formalization with AI Agents and Human Verification cites this paper.
GDPR Auto-Formalization with AI Agents and Human Verification Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T11:15:09.359635Z
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.
Observation 6a611050-8ac3-4bdb-ab58-3af590e29d50 · inbound
Beyond Imperfect Alternatives with Rulemapping: A Neuro-Symbolic Case Study on Online Hate Speech cites this paper.
Beyond Imperfect Alternatives with Rulemapping: A Neuro-Symbolic Case Study on Online Hate Speech Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T09:24:05.422250Z
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.
Observation 0320be17-92de-4ba2-9684-9a871d337af1 · inbound
Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning cites this paper.
Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T18:43:50.589345Z
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.
Observation b7fa594f-54ad-46fe-9b9c-a04a5f60b279 · inbound
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches cites this paper.
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 143
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T08:35:34.075399Z
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.
Observation 18a823f3-d181-4c8a-91a2-4ae94da15018 · inbound
Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning cites this paper.
Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 53
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T17:18:43.427281Z
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.
Observation 00835e06-b6a5-4681-8938-6470b83b3258 · inbound
Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI cites this paper.
Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T10:39:45.207554Z
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.