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Paper Citation Record · LEDGER

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation

As of 7 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2505.14015.

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

pith.paper-citation-record.v1
2505.14015 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:44:37.677071Z

measured 66 of 66 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T12:35:01.443896Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T12:36:22.403104Z

Reference resolution

65 of 65 outbound references displayed

  • verified exact2
  • verified fuzzy10
  • unresolved53
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7247335d-0a82-4454-8469-7e9926dac2e0 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:48.384827Z

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.

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Observation 21600819-baf9-4222-9606-81667b550237 · outbound

This paper cites Unfair TOS: An Automated Approach using Customized BERT.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unfair TOS: An Automated Approach using Customized BERT

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:44:42.534824Z

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=pdf_text observed=2026-08-07T15:44:29.524825Z digest=sha256:362a774172e182fe87f3d3eb8d38bb7afbe7f38cc2cef38610c4e50f511b2c0a

Observation 4cbb8d18-334c-4cfa-b7d3-055e1aad1876 · outbound

This paper cites Rapid transit systems regulations, 2025.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Rapid transit systems regulations, 2025

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:48.174889Z

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.

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Observation 5161b5c4-8a12-4a33-a9a2-18c05efa0da6 · outbound

This paper cites What Will it Take to Fix Benchmarking in Natural Language Understanding?.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation What Will it Take to Fix Benchmarking in Natural Language Understanding?

Reference 4

Resolution
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no resolver link, observed 2026-08-07T15:44:29.744748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:29.744748Z digest=sha256:d1c03b281e9ce0ed6f01ea3ca558cbe8441d392d10c58af8eda2c7611c6e1f88

Observation 9eff350c-e871-4cf4-a973-51740675abac · outbound

This paper cites Neural Legal Judgment Prediction in English.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Neural Legal Judgment Prediction in English

Reference 5

Resolution
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no resolver link, observed 2026-08-07T15:44:29.844771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:29.844771Z digest=sha256:e27964be069501a1d36cd47c7d59c023b512bc67c6287bf7e9837a14bbcbd634

Observation 40066a09-8f97-48ab-ac11-cbd7d673c1c1 · outbound

This paper cites Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases

Reference 6

Resolution
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no resolver link, observed 2026-08-07T15:44:29.974848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:29.974848Z digest=sha256:6db173f6ed46b8df0faf2a2c9e56e8328b4e6a6656038b7264a198c71c29c5ef

Observation a7928929-4296-4728-9bb8-d57fbb986c17 · outbound

This paper cites LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

Reference 7

Resolution
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no resolver link, observed 2026-08-07T15:44:30.084866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:30.084866Z digest=sha256:a1831d67ce6a81013968a94068cef19de8a0f0ef3683600c4f2965755b20fe50

Observation 950002e5-3780-484f-8a98-17966edb49ca · outbound

This paper cites Chang, X.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Chang, X

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:30.215932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:30.215932Z digest=sha256:682663b18562a6491675f4c31fdaa9fc7d499e02b9a8c672ff938059a7813c61

Observation 9c001c3f-de65-4247-bdd5-3bd3dcc05b03 · outbound

This paper cites Chase and L.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Chase and L

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:47.904995Z

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=pdf_text observed=2026-08-07T15:44:30.305017Z digest=sha256:334ce3a9aa89396f751cf4f352ffb4d3ba50e86dc0fad5edcbf9f617adc2f0fb

Observation 4fe2844c-a3f1-4679-9638-2417c3fd5176 · outbound

This paper cites Universal Self-Consistency for Large Language Model Generation.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Universal Self-Consistency for Large Language Model Generation

Reference 10

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no resolver link, observed 2026-08-07T15:44:30.406491Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:44:30.406491Z digest=sha256:4ca519205e9966214c646062e98b85e5fc930e9969f20461362ceb7a0a85f07b

Observation e056dbd0-85da-46de-ab5f-96d6ac8cc78b · outbound

This paper cites Cheng, S.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Cheng, S

Reference 11

Resolution
verified fuzzy
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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=pdf_text observed=2026-08-07T15:44:30.514729Z digest=sha256:384ee4b696a1e36127f6f9f356379761484ddfd0211a506b3f6a79c5a82a25eb

Observation 4b1d1a0c-8728-4b7f-bdf8-54eee40a46c4 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:47.405076Z

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=pdf_text observed=2026-08-07T15:44:30.604832Z digest=sha256:2d422b6a68e46dbc2e24990e9ca1d311de466d61586bcbce35b602d9fbfc8f44

Observation b8c75ba4-7a99-4926-8730-fd1a3e120c51 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:30.724738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:30.724738Z digest=sha256:56bbfb5b2adea3ea35cb3fa0fd0b1c58503bf3df6ec438d582be8a71323e172c

Observation 8cb2c3d8-1e43-468f-a148-ece2bccff244 · outbound

This paper cites Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:30.835141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:30.835141Z digest=sha256:3831b09e85e3f736b21aa25dba907cdc4ee568895fd30b9715f0bf45ac0b955c

Observation 88f52d9d-97b7-4058-8203-a0e7e0e9cc77 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:47.136736Z

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=pdf_text observed=2026-08-07T15:44:30.929174Z digest=sha256:a4e5c3e38be2418db9ad2c31bf62f61d1f9e1ff64898e12107fab2502db1683f

Observation f3e4343d-ce68-4aaf-b888-a46e546e6a76 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:46.936574Z

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=pdf_text observed=2026-08-07T15:44:31.054811Z digest=sha256:3f9df6b046b7e465d5544169d41b288b8568df5733fbfdeabebaa27d1ee61af2

Observation d12764bc-068e-4d6b-b48e-4ce9e13ea709 · outbound

This paper cites COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 17

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no resolver link, observed 2026-08-07T15:44:31.174824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:31.174824Z digest=sha256:bed3f8a4490ff184267847769c433c3d92d2c3800d69c516fcd6803b86d96532

Observation a91ba5e2-37e9-4ccb-9ce0-3429f5c448e9 · outbound

This paper cites Model Editing at Scale leads to Gradual and Catastrophic Forgetting.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Model Editing at Scale leads to Gradual and Catastrophic Forgetting

Reference 18

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no resolver link, observed 2026-08-07T15:44:31.275034Z

Source-reported events for the cited work

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Observation 975aa8fa-a992-4a1c-a1be-00a3d3f00f0c · outbound

This paper cites A Unified Framework for Model Editing.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation A Unified Framework for Model Editing

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:31.443355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:31.443355Z digest=sha256:096b2c2ee2043b407de013b9caeeee7334d1e2e4aecb49471a6569fc4f3c65eb

Observation 56638672-e87b-4255-9b35-09b7f2ccd615 · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:31.519859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:31.519859Z digest=sha256:6830c88eb570c0722e5627a8b693158bd3d3b396003f1ab165330a35daee601f

Observation a30226a7-3a24-4043-b9fb-8188a90e12d8 · outbound

This paper cites Hartvigsen, S.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Hartvigsen, S

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:46.625405Z

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=pdf_text observed=2026-08-07T15:44:31.685568Z digest=sha256:4daf996938df19d0bcb2fa1b4e0d7d08b6cedd059f96648ed15fa839ae276b78

Observation c8a72d20-168f-4318-97bf-c08ad96ed6b8 · outbound

This paper cites ORPO: Monolithic Preference Optimization without Reference Model.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation ORPO: Monolithic Preference Optimization without Reference Model

Reference 22

Resolution
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no resolver link, observed 2026-08-07T15:44:31.814821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:31.814821Z digest=sha256:15076a613ab1f1df023a286bbfd109d01c49761b62503a67ab9447ef3ddced4b

Observation e6b1f491-6130-41a6-90f1-628f8b73c384 · outbound

This paper cites AI safety via debate.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation AI safety via debate

Reference 23

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no resolver link, observed 2026-08-07T15:44:32.014818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:32.014818Z digest=sha256:9ad002c3d8a7cd685642eebdd429b2e2c0f5906658149ce4337a42e64b53deb4

Observation 064ca0e9-114e-4142-ba77-d30629fd2e19 · outbound

This paper cites Jiang, Y.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Jiang, Y

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:46.235043Z

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=pdf_text observed=2026-08-07T15:44:32.113880Z digest=sha256:72e4bb886893e24d41c985110308137345496151afe78ae6c06fe3cf98d8fc53

Observation fbca9371-21f0-4139-a1b4-79cea8b624d3 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:46.016568Z

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.

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Observation 06e93608-3a79-4d69-bf07-c0063b1a6d72 · outbound

This paper cites Debating with More Persuasive LLMs Leads to More Truthful Answers.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:32.321541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:32.321541Z digest=sha256:251ec7725202c3ca58448387d865846ff3ac6d3640b69b49297aa3125adcee4a

Observation 80f18f7e-4beb-4140-85ab-dfeafcfe8d73 · outbound

This paper cites Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:32.474657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:32.474657Z digest=sha256:41fc1455d93204b085949af5e3dc58e91870b920d89c111ce85dcbf6d292aa1b

Observation b9fe08c8-d9ba-4670-bfd3-6211ae38b990 · outbound

This paper cites Kojima, S.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Kojima, S

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:45.575197Z

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=pdf_text observed=2026-08-07T15:44:32.623566Z digest=sha256:e6fe3526756470b6d0e19d7cdb96aa9ddb5daf01e1b967185d8683e16a8ab06a

Observation 7bb2f2e6-3c13-46e5-8699-8abeb4d6afb7 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:45.254742Z

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=pdf_text observed=2026-08-07T15:44:32.734801Z digest=sha256:ec3a163ae1f9c71ca0e712058efad4d9cf39ec2ab601b064659582859b22995e

Observation 92e33e46-472e-4fe8-97c2-e1d76f250906 · outbound

This paper cites Should We Really Edit Language Models? On the Evaluation of Edited Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Should We Really Edit Language Models? On the Evaluation of Edited Language Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:32.886813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:32.886813Z digest=sha256:64f7c609efcad897ebf5e29b40da78170ef4c971def07d9dbe760d41f224283f

Observation 8b72b3f5-1ea1-4ebe-96ac-de5cfa7d3832 · outbound

This paper cites PMET: Precise Model Editing in a Transformer.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation PMET: Precise Model Editing in a Transformer

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:33.047505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:33.047505Z digest=sha256:2dec02ee178d2d607eed0b86a79bf29b229e9714295ad8012a1419bd40f68bbf

Observation 37612b5a-76f8-4b62-9784-90f726004da5 · outbound

This paper cites Making Large Language Models Better Reasoners with Step-Aware Verifier.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Making Large Language Models Better Reasoners with Step-Aware Verifier

Reference 32

Resolution
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no resolver link, observed 2026-08-07T15:44:33.398334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:33.398334Z digest=sha256:04eef773f05ce02742dc5ae0371f0e9b970cae6f8534975c64722e291fd4fc32

Observation 20ee13b7-e2d9-4c36-8e63-1a6b4e7cfdc3 · outbound

This paper cites Chain-of-Dictionary Prompting Elicits Translation in Large Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Chain-of-Dictionary Prompting Elicits Translation in Large Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:33.605130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:33.605130Z digest=sha256:54bf0eb7cd6fbec6b1685abed8e055323cb6cb83213ea2c6d2c17dad3f9a3cac

Observation 09d04ff8-f73a-41a5-a420-98974740b382 · outbound

This paper cites Tree of Attacks: Jailbreaking Black-Box LLMs Automatically.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

Reference 34

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no resolver link, observed 2026-08-07T15:44:33.763604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:33.763604Z digest=sha256:ee72c5130d682197ac4a5a42ce538d81f828d08076b36a759826a56a5adfa4ce

Observation e1055890-ca98-4ef2-8938-2b7b0f269e65 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:44.625379Z

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=pdf_text observed=2026-08-07T15:44:33.894767Z digest=sha256:89d748790f71dcc9fefef83de0d9686585134338e7ebb5c7343355b9bdce396c

Observation 738ef0ec-2700-40fd-b2a6-aca504a2432c · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 36

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unresolved
raw_fallback, observed 2026-08-07T15:44:44.354751Z

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=pdf_text observed=2026-08-07T15:44:34.016544Z digest=sha256:9db91fe284fddd4a7a4cfd01486802c0cd2cf96a38df5eb21a2ce50425cc445d

Observation 48d8b6e2-2819-425e-a5c2-3175e8f9f7e9 · outbound

This paper cites Debate Helps Supervise Unreliable Experts.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Debate Helps Supervise Unreliable Experts

Reference 37

Resolution
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no resolver link, observed 2026-08-07T15:44:34.164341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:34.164341Z digest=sha256:88a26a98d43ba60a37b604dd52da27a6bf0cbd8e36c0a3e20a31bb11a217b4f8

Observation d3404b2a-bb76-4d4b-b76d-62d70d98a946 · outbound

This paper cites Mitchell, C.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Mitchell, C

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:44.150787Z

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=pdf_text observed=2026-08-07T15:44:34.274743Z digest=sha256:0a43b71f58ad60099209e7f2cf5858f1125e25922ecb89d185e4d44a135272a0

Observation 3f494adb-90da-4af7-8e3a-3f10545403a5 · outbound

This paper cites UniAdapt: A Universal Adapter for Knowledge Calibration.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation UniAdapt: A Universal Adapter for Knowledge Calibration

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:44:40.290135Z

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=pdf_text observed=2026-08-07T15:44:34.454265Z digest=sha256:bc6b0fc0f628df5866ea04d70fadf2d60be1f0c6db14a96910609bf8681aad03

Observation b70f0f31-64cd-4c41-94c1-6b43de2084b6 · outbound

This paper cites Ouyang, J.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Ouyang, J

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:34.612802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:34.612802Z digest=sha256:b2c54dc969d16b3967c19dd03dea00d5700172306d04f0d8362f03b74b7acd60

Observation 1a51543b-bc3b-4d61-a04d-51f6712a87ed · outbound

This paper cites Red Teaming Language Models with Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Red Teaming Language Models with Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:34.752374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:34.752374Z digest=sha256:0e4999b36c994c636ccf8ce746a9fefbda249ce198db40728ab3e960f40e3d07

Observation 1cb88350-9ca5-4d93-b38e-f42585930aee · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Measuring and Narrowing the Compositionality Gap in Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:34.924840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:34.924840Z digest=sha256:87debfe730f11d830a1ab2392ff4b40d75f63284c94923ab65c9c98a25231c46

Observation f81f5ed6-c2cd-469f-b6fb-2fd5113df9d3 · outbound

This paper cites Rafailov, A.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Rafailov, A

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:43.874750Z

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=pdf_text observed=2026-08-07T15:44:35.024865Z digest=sha256:e6e52a6bb51f4ff9064574dd2ef385f62c881786e3353d026d7f1ca2feb371b8

Observation 53a5de5b-942f-4d91-b782-2aace0dcdaf5 · outbound

This paper cites Rafailov, A.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Rafailov, A

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:35.172088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:35.172088Z digest=sha256:3b051aaa79b19eb860d1fe9d5c4ce04f5278838dad7cafd57933abe004f0edf8

Observation 20d7b2ee-4aa6-4b57-8fd1-8bf9f1f640ee · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:35.309440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:35.309440Z digest=sha256:4bdd54b2d261e8dcbfd357cdca25d4b794a694c86b37876dccd23bea01ba83e2

Observation ff90a715-450e-4fd5-8e3f-62c82492fc60 · outbound

This paper cites Selective Annotation Makes Language Models Better Few-Shot Learners.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:35.445205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:35.445205Z digest=sha256:317955c1038d56e5cce3a225ae95eb1054aead6355289976dd7ccdf081fc2f15

Observation 77ba7ed5-64fe-4289-83d0-8b402693cf54 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:43.632980Z

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=pdf_text observed=2026-08-07T15:44:35.515086Z digest=sha256:9873dfa9e1f1db3fe676ae9f93e786d126a28669f23b4a7d5368f4da50419dd0

Observation ca39aa72-e407-44dc-b4ad-9d64a5959832 · outbound

This paper cites Tuggener, P.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Tuggener, P

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:43.474959Z

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=pdf_text observed=2026-08-07T15:44:35.628184Z digest=sha256:9a53aa299f78aeed648957a084b1e0f29db91efcc73d8c16d11ea4486a827a26

Observation f134ede9-2208-40fd-9818-f9904e380b0e · outbound

This paper cites Soft Self-Consistency Improves Language Model Agents.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Soft Self-Consistency Improves Language Model Agents

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:35.719367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:35.719367Z digest=sha256:0649c47a86b0c1de086253e3ce6cd9b3f403b2ced8d8251205548fb03a364695

Observation 49f7b1c1-3ae8-4df4-89d0-93d05f5b38af · outbound

This paper cites Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:35.854809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:35.854809Z digest=sha256:5ae9e522bf5236fe201585f8ab1e72f5af59a2c382be706a95cd543c423c5801

Observation 01f3a777-bccc-40d4-932e-efff53fce652 · outbound

This paper cites Boosting Language Models Reasoning with Chain-of-Knowledge Prompting.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Boosting Language Models Reasoning with Chain-of-Knowledge Prompting

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:35.955444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:35.955444Z digest=sha256:5c0cbfad4eede65cc283d692c13fb702e0ea2505362a1344312ccbd76bca00ac

Observation 6e2583d8-e779-42d7-b879-4e1ab81c1394 · outbound

This paper cites A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.085184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.085184Z digest=sha256:3efc50ecc60a62bf2937c414873ecf8ed78dec1f1f27e878d6267691c9170175

Observation 6a989074-efb8-44ee-8131-38bff7cd2341 · outbound

This paper cites WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.184817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.184817Z digest=sha256:bbbd02d82c57fb7e0aa065bfa159f97a31f44a8bd1152eb36749482ada6219e5

Observation fc5e7b5c-f91e-47ac-8787-fdc987607890 · outbound

This paper cites LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.284825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.284825Z digest=sha256:886add1aa8cc3b96cb1f2dadd0ea77b14d138019568c137caf5d7390113299e8

Observation 216b3c8a-152f-448f-90c2-11b557709b73 · outbound

This paper cites MEMoE: Enhancing Model Editing with Mixture of Experts Adaptors.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation MEMoE: Enhancing Model Editing with Mixture of Experts Adaptors

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.382919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.382919Z digest=sha256:0706fbbe7117b868ce119b138f0faffc57179f6857124f57133800c2123dfb16

Observation a54f0538-afc4-4b09-9a25-277f5d15e0bf · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.534881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.534881Z digest=sha256:e5177f921024b030a73dda3e4c5e225609fd262d93de0ea335f0434ae8fea742

Observation 8eebfd99-b86f-4f24-845c-3a3b109725dc · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.671264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.671264Z digest=sha256:8aecbce9ed352bb6a90bc4a01bbcf8f243a5161d0df8ecf2c2a96035048c6c6f

Observation 95f54e3b-6556-475f-9b43-978d43070305 · outbound

This paper cites Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Reference 58

Resolution
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no resolver link, observed 2026-08-07T15:44:36.826281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.826281Z digest=sha256:33dddae99a96d394072ae5ded82dcb91a97c8da6046fc93c61342f1a5a80f63b

Observation de37bb04-3c96-47a9-b531-94c6eba97a24 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:36.904818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:36.904818Z digest=sha256:434d1769baa1dbb4574121f7e6287f15877adb8bd3b33dccdc9ea4c0ef3abdbd

Observation 3c102c70-b362-44a0-a9f1-7449abaddfb7 · outbound

This paper cites GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:37.034806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:37.034806Z digest=sha256:fcd9594f3b4738573a3ce3aef4ea62bd994130f340fdb0b92caf7f6173e1ee0e

Observation 45bdeb3b-2812-481b-8b48-28e4a965848e · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 61

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:43.168672Z

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=pdf_text observed=2026-08-07T15:44:37.254837Z digest=sha256:c35a96fd029dbdecc981b07eaa5b2d17b14e93cbae963041600e82e9c90798f3

Observation 8942558c-693d-41d1-93c8-aa22b577bdf6 · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Automatic Chain of Thought Prompting in Large Language Models

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:37.360605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:37.360605Z digest=sha256:494157ca4a817d994c8e12acf80d4ad45eb46c577824f416c5da66ae3c22b83f

Observation 7d11f460-af10-4a1d-9ed5-1dc11ca9e0de · outbound

This paper cites ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-07T15:44:37.514951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:44:37.514951Z digest=sha256:57d28a59e3a20a7b6ec5b62499a8b1c6444a6ae0d8ebfad196ee7f3698dd0c1c

Observation db94feb9-7413-4a1c-89ae-4fb608dcd4f1 · outbound

This paper cites Jason eats a burger on train.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Jason eats a burger on train

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:44:42.874304Z

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=pdf_text observed=2026-08-07T15:44:37.677071Z digest=sha256:7df4fe403868524545fc89e8482bdf4227553cefa4a4f7725c63b8bd46f3c0f3

Observation 03a6f583-5513-4f88-a652-13be331d70f5 · outbound

This paper cites an unresolved cited work.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Unresolved cited work

Reference 2023

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:44:44.943445Z

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=pdf_text observed=2026-08-07T15:44:33.215993Z digest=sha256:ab7e6b9624d5c14a2dcfaf98ccf55a6861615ae49ba5eae3164aaa344a6a4fcb

Pith citing papers

Observation 9087ef2b-6f19-42b1-8924-78895d26c02e · inbound

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models cites this paper.

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-18T12:36:22.406321Z

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=pdf_text observed=2026-05-18T12:35:01.443896Z digest=sha256:55e92fd405dcfe3390004fbd9516b331c6507ac0cbbd47c8f0b1642304bba21f