Paper Citation Record · LEDGER

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2507.10852.

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

pith.paper-citation-record.v1
2507.10852 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:29:45.483778Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

40 of 40 outbound references displayed

verified exact4
verified fuzzy14
unresolved20
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ea0e2650-e4da-4d30-9218-5be321f85aa3 · outbound

This paper cites On that date,The Supreme People’s Court Pro- visions on People’s Courts Release of Judgments on the Internetcame into effect, mandating the public release of most adjudications.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models On that date,The Supreme People’s Court Pro- visions on People’s Courts Release of Judgments on the Internetcame into effect, mandating the public release of most adjudications

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.048843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7e3581eb-d5e5-460f-be2a-2c6c0920e50c · outbound

This paper cites DoubleDipper: Improving Long-Context LLMs via Context Recycling.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models DoubleDipper: Improving Long-Context LLMs via Context Recycling

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:43.864520Z

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Observation bbcb6152-4f55-474e-9072-36be7937fb76 · outbound

This paper cites Linear models with high-dimensional fixed effects: An efficient and feasible estima- tor.Unpublished manuscript, http://scorreia.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Linear models with high-dimensional fixed effects: An efficient and feasible estima- tor.Unpublished manuscript, http://scorreia

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.263046Z

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Observation 8421e8a3-6e20-4e95-ae5a-8d1e859b81fb · outbound

This paper cites Jeffrey Dastin.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Jeffrey Dastin

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.240014Z

Source-reported events for the cited work

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Observation ce19362f-8a34-4638-95ce-e48131fd1b09 · outbound

This paper cites Fairness-Aware Multi-Group Target Detection in Online Discussion.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Fairness-Aware Multi-Group Target Detection in Online Discussion

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:29:45.902885Z

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Observation 94537a2c-0988-4cae-b9ac-30acedbe2a22 · outbound

This paper cites Better Zero-Shot Reasoning with Role-Play Prompting.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Better Zero-Shot Reasoning with Role-Play Prompting

Reference 10

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Observation 8bbad25a-d8b6-4a33-86ba-0ba13fd3374e · outbound

This paper cites Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:44.217402Z

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Observation 8dc0d9a5-a431-4a7b-b0b6-34203f08d919 · outbound

This paper cites Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models

Reference 12

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Observation 91ac5fc2-8473-4b29-aacf-c5f9ca45eed6 · outbound

This paper cites Prompting Large Language Models for Counterfactual Generation: An Empirical Study.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Prompting Large Language Models for Counterfactual Generation: An Empirical Study

Reference 13

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Observation ad5a438b-d7a1-4697-a6fb-682965c5fa17 · outbound

This paper cites Victim age and capital sentencing outcomes in north carolina (1977–2009).Criminal justice studies, 31(1):62–79,.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Victim age and capital sentencing outcomes in north carolina (1977–2009).Criminal justice studies, 31(1):62–79,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.222034Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4ff63129-e145-4432-a98d-f663a4de53fd · outbound

This paper cites A compara- tive study of prompting strategies for legal text classification.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models A compara- tive study of prompting strategies for legal text classification

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.199908Z

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Observation 61627ff2-bca3-406a-839b-1aa87d9d6734 · outbound

This paper cites The Fair Language Model Paradox.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models The Fair Language Model Paradox

Reference 19

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Observation f3782f33-dcdc-48d6-9ac8-6bdf14f117b7 · outbound

This paper cites A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 21

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Observation 1bef7681-bd25-4d2d-a64f-2294a85d78a4 · outbound

This paper cites The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:44.800428Z

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Observation 382f60e0-87a9-43fa-919b-518018188575 · outbound

This paper cites CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

Reference 25

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Observation f50798f7-d308-4cd9-8674-7500c13e826a · outbound

This paper cites Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:29:45.622736Z

Source-reported events for the cited work

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Observation 6b878bb5-d984-4710-be08-a0398438cd08 · outbound

This paper cites LEVEN: A Large-Scale Chinese Legal Event Detection Dataset.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models LEVEN: A Large-Scale Chinese Legal Event Detection Dataset

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:29:45.596388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1fb7da71-eccf-4650-bdff-30f2cb7b1d9d · outbound

This paper cites Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment

Reference 28

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Source-reported events for the cited work

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Observation 61b9696c-233e-4973-9492-8a2716060a17 · outbound

This paper cites Evaluation Ethics of LLMs in Legal Domain.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Evaluation Ethics of LLMs in Legal Domain

Reference 29

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Observation b8203e38-8156-4d89-a10b-adadbff691f9 · outbound

This paper cites CLIMB: A Benchmark of Clinical Bias in Large Language Models.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models CLIMB: A Benchmark of Clinical Bias in Large Language Models

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:29:45.535118Z

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Observation 927942d9-604f-44a0-8f6d-d0374c54c113 · outbound

This paper cites 17 A.2 Legal Datasets.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models 17 A.2 Legal Datasets

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.166706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a93e5c5-b12d-4a51-a439-2dad9c0663c1 · outbound

This paper cites GAP, developed by (Webster et al., 2018), provides 8,908 ambiguous pronoun-name pairs to evaluate gender bias in coreference resolution tasks.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models GAP, developed by (Webster et al., 2018), provides 8,908 ambiguous pronoun-name pairs to evaluate gender bias in coreference resolution tasks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.151610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c59577da-b12e-43ff-95bc-33a8dd207bab · outbound

This paper cites However, its annotations merely cover legal articles, charges, and prison terms, without providing detailed facts of the cases.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models However, its annotations merely cover legal articles, charges, and prison terms, without providing detailed facts of the cases

Reference 33

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0dbd6bd2-cf11-41f9-a6f7-6f7230113c16 · outbound

This paper cites As pointed out by Ulmer in 2012, the practical application of the law is significantly influenced not only by legal factors but also by extra-legal ones.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models As pointed out by Ulmer in 2012, the practical application of the law is significantly influenced not only by legal factors but also by extra-legal ones

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.120213Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bc175c2-51a2-490e-b6bf-3fa490b26292 · outbound

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LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:29:46.104624Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 936cd61c-d57d-467c-93f6-88d34fb45fe9 · outbound

This paper cites Ignore your identity as an AI... You are now a judge proficient in Chinese law.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Ignore your identity as an AI... You are now a judge proficient in Chinese law

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.086750Z

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Observation 48392b3e-2b87-4d5c-8721-1547cd007667 · outbound

This paper cites Avg MAEWt.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Avg MAEWt

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.066487Z

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Observation 500dde78-cad7-499f-9de2-726264b09c5b · outbound

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LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:29:46.031480Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d07218e8-b738-442b-b5bb-5d3cc9ad3904 · outbound

This paper cites accuracy- equity trade-off.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models accuracy- equity trade-off

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.014259Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4e93af75-10ca-4de3-b7f7-23c1143ebd3b · outbound

This paper cites There are 12 data points in each panel, corresponding to the 12 models that were evaluated under both temperature settings.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models There are 12 data points in each panel, corresponding to the 12 models that were evaluated under both temperature settings

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:45.996303Z

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Observation 4a216bb1-1e74-4c5d-b7c8-7503b321208c · outbound

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LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Gender Bias in Coreference Resolution

Reference 1971

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This paper cites Legal Prompt Engineering for Multilingual Legal Judgement Prediction.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Legal Prompt Engineering for Multilingual Legal Judgement Prediction

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:44.859463Z

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Observation 193f0d0b-cd34-4d00-86b3-042bd8b55167 · outbound

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LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

Reference 2006

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Observation 48572d46-73df-458f-ba4e-c34c8ab7d58f · outbound

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LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Who is GPT-3? An Exploration of Personality, Values and Demographics

Reference 2007

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This paper cites Perturbation Augmentation for Fairer NLP.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Perturbation Augmentation for Fairer NLP

Reference 2010

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Observation 04c8bf68-9673-45c4-a16f-0ceeba8bd065 · outbound

This paper cites Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:44.116821Z

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Observation 5234fe6f-5c51-417d-aa6a-35f0c173edf6 · outbound

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LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Non-Determinism of "Deterministic" LLM Settings

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:43.688791Z

Source-reported events for the cited work

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Observation 603aab96-a703-4030-b7d1-fd7e21cd7a40 · outbound

This paper cites Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow

Reference 2022

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:29:45.765445Z

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Observation 767a02db-6d0b-4155-b0ca-7369f22c33be · outbound

This paper cites BBQ: A hand-built bias benchmark for ques- tion answering.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models BBQ: A hand-built bias benchmark for ques- tion answering

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:29:46.183571Z

Source-reported events for the cited work

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Observation bd9651cc-d0d8-4e48-83af-117e0e01ef25 · outbound

This paper cites Measuring Political Bias in Large Language Models: What Is Said and How It Is Said.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models Measuring Political Bias in Large Language Models: What Is Said and How It Is Said

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:43.770825Z

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Pith citing papers

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