Paper Citation Record · LEDGER

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI

As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2606.18021.

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

pith.paper-citation-record.v1
2606.18021 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T00:34:53.489694Z

measured 22 of 22 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy0
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45d16374-916a-4181-bbba-d7b00230dc1a · outbound

This paper cites Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics , year =

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f98e003-7678-40f1-b0a5-c37e34207e1b · outbound

This paper cites Proceedings of NeurIPS Datasets and Benchmarks , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of NeurIPS Datasets and Benchmarks , year =

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 062ace10-d23f-4a16-9cb0-260ea7674edd · outbound

This paper cites Proceedings of EMNLP , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of EMNLP , year =

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6622b9ff-2b89-4deb-b361-bc34e4b45d8d · outbound

This paper cites Lynx: An Open Source Hallucination Evaluation Model.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Lynx: An Open Source Hallucination Evaluation Model

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:28:58.609098Z

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 39b03420-626a-435f-bb7b-9451693948dd · outbound

This paper cites LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:28:58.612291Z

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 409ded0d-0e36-48e9-87ad-9d4baddbb7af · outbound

This paper cites Proceedings of the Natural Legal Language Processing Workshop , pages =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of the Natural Legal Language Processing Workshop , pages =

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 642de15f-9d8f-4404-ac6c-5b4f8c52aa3a · outbound

This paper cites Proceedings of the Natural Legal Language Processing Workshop , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of the Natural Legal Language Processing Workshop , year =

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 179f1c48-a3fd-4458-8922-8ce7efe460cc · outbound

This paper cites Proceedings of the Natural Legal Language Processing Workshop , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of the Natural Legal Language Processing Workshop , year =

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71f8d809-ac27-440b-a970-46410dc81505 · outbound

This paper cites , title =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI , title =

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5ee0db1-eef0-4590-a3e5-49e0307516bf · outbound

This paper cites and Ho, Daniel E.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI and Ho, Daniel E

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb2f5695-f13d-4caf-887a-5aacfcb008f8 · outbound

This paper cites Mikail and Canbaz, M.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Mikail and Canbaz, M

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a296c01-0f9e-4adf-85eb-b5724b39c867 · outbound

This paper cites and Negreanu, Carina Suzana and Boxall, Kitty and Mincu, Diana , title =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI and Negreanu, Carina Suzana and Boxall, Kitty and Mincu, Diana , title =

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ab53ff3-1f5e-46ab-a3c8-db12beaed6ab · outbound

This paper cites Proceedings of NeurIPS , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of NeurIPS , year =

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b734eb64-b097-4cac-97ed-19e6ebb89c29 · outbound

This paper cites and Mordatch, Igor , title =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI and Mordatch, Igor , title =

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30a1c0e8-3b83-4ceb-bd45-fbbbe7360285 · outbound

This paper cites Proceedings of COLING , pages =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of COLING , pages =

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95fd528f-85e1-49ac-859d-c27d63399cde · outbound

This paper cites Findings of EMNLP , pages =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Findings of EMNLP , pages =

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edacdca4-77dc-4a51-8969-32bfff52b297 · outbound

This paper cites Proceedings of ACL , pages =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of ACL , pages =

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42249dda-4923-4ade-9d11-59c3a56188c1 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:28:58.620953Z

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 c75c7ef7-8a7a-4b74-86db-ca9cad58b924 · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:28:58.615202Z

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 c321911d-d959-457b-b948-024cbbc977a3 · outbound

This paper cites Proceedings of ICLR , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of ICLR , year =

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0ea5dcd-b591-4b66-a1de-62694714244b · outbound

This paper cites Proceedings of the Natural Legal Language Processing Workshop , year =.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Proceedings of the Natural Legal Language Processing Workshop , year =

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T00:34:53.489694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f4715bb-7619-4f89-8245-005d11c5f698 · outbound

This paper cites Lexam: Benchmarking legal reasoning on 340 law exams.

LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI Lexam: Benchmarking legal reasoning on 340 law exams

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:28:58.618132Z

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.

Pith citing papers

No inbound Pith citation observations are available.