{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AD2S24GYNP4KANNZ2SUECAMPJI","short_pith_number":"pith:AD2S24GY","schema_version":"1.0","canonical_sha256":"00f52d70d86bf8a035b9d4a841018f4a3e1857ae38ba910aca5be6511bd711ca","source":{"kind":"arxiv","id":"2310.09241","version":1},"attestation_state":"computed","paper":{"title":"Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changlong Sun, Fei Wu, Kun Kuang, Siying Zhou, Weiming Lu, Xiaozhong Liu, Yating Zhang, Yifei Liu, Yiquan Wu","submitted_at":"2023-10-13T16:47:20Z","abstract_excerpt":"Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI, i.e., predicting the judgment of the case in terms of case fact description. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Thus, it is worthwhile to explore the utilization of precedents in the LJP. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. These can be broken down into two categories: large language models (LLMs) and domain-specific models. LLMs are"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.09241","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-13T16:47:20Z","cross_cats_sorted":[],"title_canon_sha256":"3e9ca429c6d2c1c40dcded0f7ebafb028b2dcf17a1a121b39bc780565aa6ce8c","abstract_canon_sha256":"40562d65afe1ff1daf6f7b67ae8887eab7fbd4114214c3916bc93742fac55dae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:41.693097Z","signature_b64":"Idf2TdMbVyGQ3eYPdZAeicUc1xXVCax/gyvdlHyxqO49oH2jkEFcIoxHLF0Dj3nlWdgXh0JakMc7XHItEQgkCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00f52d70d86bf8a035b9d4a841018f4a3e1857ae38ba910aca5be6511bd711ca","last_reissued_at":"2026-07-05T07:00:41.692590Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:41.692590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changlong Sun, Fei Wu, Kun Kuang, Siying Zhou, Weiming Lu, Xiaozhong Liu, Yating Zhang, Yifei Liu, Yiquan Wu","submitted_at":"2023-10-13T16:47:20Z","abstract_excerpt":"Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI, i.e., predicting the judgment of the case in terms of case fact description. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Thus, it is worthwhile to explore the utilization of precedents in the LJP. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. These can be broken down into two categories: large language models (LLMs) and domain-specific models. LLMs are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09241","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.09241/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.09241","created_at":"2026-07-05T07:00:41.692655+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.09241v1","created_at":"2026-07-05T07:00:41.692655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09241","created_at":"2026-07-05T07:00:41.692655+00:00"},{"alias_kind":"pith_short_12","alias_value":"AD2S24GYNP4K","created_at":"2026-07-05T07:00:41.692655+00:00"},{"alias_kind":"pith_short_16","alias_value":"AD2S24GYNP4KANNZ","created_at":"2026-07-05T07:00:41.692655+00:00"},{"alias_kind":"pith_short_8","alias_value":"AD2S24GY","created_at":"2026-07-05T07:00:41.692655+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00783","citing_title":"LegalChainReasoner: A Legal Chain-guided Framework for Criminal Judicial Opinion Generation","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI","json":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI.json","graph_json":"https://pith.science/api/pith-number/AD2S24GYNP4KANNZ2SUECAMPJI/graph.json","events_json":"https://pith.science/api/pith-number/AD2S24GYNP4KANNZ2SUECAMPJI/events.json","paper":"https://pith.science/paper/AD2S24GY"},"agent_actions":{"view_html":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI","download_json":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI.json","view_paper":"https://pith.science/paper/AD2S24GY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.09241&json=true","fetch_graph":"https://pith.science/api/pith-number/AD2S24GYNP4KANNZ2SUECAMPJI/graph.json","fetch_events":"https://pith.science/api/pith-number/AD2S24GYNP4KANNZ2SUECAMPJI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI/action/storage_attestation","attest_author":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI/action/author_attestation","sign_citation":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI/action/citation_signature","submit_replication":"https://pith.science/pith/AD2S24GYNP4KANNZ2SUECAMPJI/action/replication_record"}},"created_at":"2026-07-05T07:00:41.692655+00:00","updated_at":"2026-07-05T07:00:41.692655+00:00"}