Paper Citation Record · LEDGER

DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2309.11325.

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

pith.paper-citation-record.v1
2309.11325 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 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 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:47.230735Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:59:19.868722Z

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 18d225d4-54bc-4aee-a8eb-a965537c1944 · inbound

A Survey on Knowledge Distillation of Large Language Models cites this paper.

A Survey on Knowledge Distillation of Large Language Models DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 257

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T23:31:11.486679Z

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 3e21a8cf-657b-4856-8bef-2136eab9a4ac · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 195

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:32:17.478365Z

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 93cd4be0-0cf7-438e-b8c0-6dc22e1c589f · inbound

PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling cites this paper.

PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.690395Z

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 ec3e91e0-8e34-4731-8f45-03fc67016faf · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 286

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:08:35.879419Z

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 0fbf9f14-22a4-4ea8-be2e-43b7aa2a1613 · inbound

AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios cites this paper.

AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:47.230735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 953eeec4-bd06-40a2-bf4d-992683bb46c2 · inbound

EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding cites this paper.

EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:18.762796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3df008a-d87d-4b2d-94f5-ec0652849643 · inbound

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices cites this paper.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:03.268870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7799ae0-b84f-437d-8650-0d001e4f2d6b · inbound

When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance cites this paper.

When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 205

Resolution: unresolved; no resolver link, observed 2026-08-06T18:37:11.293301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bceebe9-2661-45fd-98a8-0f5e789d64cf · inbound

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study cites this paper.

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:22:21.232106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ace7340-eeb6-4687-9183-43c0b14e3f96 · inbound

SVGen: Interpretable Vector Graphics Generation with Large Language Models cites this paper.

SVGen: Interpretable Vector Graphics Generation with Large Language Models DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T23:59:29.519552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62efc341-c659-4a15-b3c1-427d5da09328 · inbound

LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases cites this paper.

LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T22:48:38.287995Z

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 40cc7cab-2c87-462d-becd-1f861144b07a · inbound

VLegal-Bench: Cognitively Grounded Benchmark for Vietnamese Legal Reasoning of Large Language Models cites this paper.

VLegal-Bench: Cognitively Grounded Benchmark for Vietnamese Legal Reasoning of Large Language Models DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:38:34.173591Z

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 698e83ed-bd67-4e19-bd62-05d3dbc38cf9 · inbound

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning cites this paper.

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T10:57:46.861359Z

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 a8862b74-64f3-4a52-9fed-031c20242023 · inbound

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice cites this paper.

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:00.640807Z

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 67ac3a1a-d77f-4f92-aeca-685a11b26bee · inbound

RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game cites this paper.

RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:50:58.757138Z

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 571762a2-1c13-4d3f-a25e-e5dd3eb4d85a · inbound

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models cites this paper.

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:16:25.978199Z

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 832f54c2-1fbc-4fa8-906f-ff973d9be835 · 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 DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 32

Resolution: malformed identifier; arxiv_id, observed 2026-06-29T18:43:50.581003Z

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 29058fe9-b49b-4a97-8787-c97b9e6a1e23 · inbound

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning cites this paper.

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 13

Resolution: malformed identifier; arxiv_id, observed 2026-06-29T13:23:28.251857Z

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 9bc49c3d-e9cd-4169-963d-76a78640ebfc · inbound

An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination cites this paper.

An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:18:03.655705Z

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 367108e7-7ca9-4547-bfa9-f2319b556411 · inbound

LegalWorld: A Life-Cycle Interactive Environment for Legal Agents cites this paper.

LegalWorld: A Life-Cycle Interactive Environment for Legal Agents DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:59:19.873586Z

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 1e734676-426f-4feb-9734-724d1c6dc678 · inbound

NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO cites this paper.

NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-11T22:45:47.429470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93769064-59df-4fd4-aa35-46300b928e1e · inbound

NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO cites this paper.

NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T08:49:45.991281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83b901b2-45f7-4d04-86cf-772714be97c9 · inbound

Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory cites this paper.

Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-31T23:07:18.133745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.