{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4TJUA2R5W6YLVVDZYCFMTN4B76","short_pith_number":"pith:4TJUA2R5","schema_version":"1.0","canonical_sha256":"e4d3406a3db7b0bad479c08ac9b781ff9546b1939e132bbffca5e9509de485f7","source":{"kind":"arxiv","id":"2312.05762","version":1},"attestation_state":"computed","paper":{"title":"Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fang Wang, Jitai Hao, Kai Zhao, Pengjie Ren, Shen Gao, Yougang Lyu, Zhaochun Ren, Zhumin Chen, Zihan Wang","submitted_at":"2023-12-10T04:46:30Z","abstract_excerpt":"Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e.g., law articles, charges, and terms of penalty) for single-defendant cases. To address this problem, we propose the task of multi-defendant LJP, which aims to automatically predict the judgment results for each defendant of multi-defendant cases. Two challenges arise with the task of multi-defendant LJP: (1) indistinguishable judgment results among various defendants; and (2) the "},"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":"2312.05762","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-10T04:46:30Z","cross_cats_sorted":[],"title_canon_sha256":"b47e3ba6d49462bdf2ef0dba5d745c7116eb8c52a1e3585b211dfac18177dcd1","abstract_canon_sha256":"9c58251a0d91c4195f1db94fb3e9c2d0e3b0a4dd3e53abffd2d7294f85d4430e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:32.431492Z","signature_b64":"FyVUZ/PwhGnckhBTaOlVcjYuUglYnvRoh3bmWawFxLZOEcZlMEpWZXvoaRoPmwDDvCabMg7wjysTzkpcFOmgBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4d3406a3db7b0bad479c08ac9b781ff9546b1939e132bbffca5e9509de485f7","last_reissued_at":"2026-07-05T07:22:32.431024Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:32.431024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fang Wang, Jitai Hao, Kai Zhao, Pengjie Ren, Shen Gao, Yougang Lyu, Zhaochun Ren, Zhumin Chen, Zihan Wang","submitted_at":"2023-12-10T04:46:30Z","abstract_excerpt":"Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e.g., law articles, charges, and terms of penalty) for single-defendant cases. To address this problem, we propose the task of multi-defendant LJP, which aims to automatically predict the judgment results for each defendant of multi-defendant cases. Two challenges arise with the task of multi-defendant LJP: (1) indistinguishable judgment results among various defendants; and (2) the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.05762","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/2312.05762/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":"2312.05762","created_at":"2026-07-05T07:22:32.431085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.05762v1","created_at":"2026-07-05T07:22:32.431085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.05762","created_at":"2026-07-05T07:22:32.431085+00:00"},{"alias_kind":"pith_short_12","alias_value":"4TJUA2R5W6YL","created_at":"2026-07-05T07:22:32.431085+00:00"},{"alias_kind":"pith_short_16","alias_value":"4TJUA2R5W6YLVVDZ","created_at":"2026-07-05T07:22:32.431085+00:00"},{"alias_kind":"pith_short_8","alias_value":"4TJUA2R5","created_at":"2026-07-05T07:22:32.431085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.14011","citing_title":"Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76","json":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76.json","graph_json":"https://pith.science/api/pith-number/4TJUA2R5W6YLVVDZYCFMTN4B76/graph.json","events_json":"https://pith.science/api/pith-number/4TJUA2R5W6YLVVDZYCFMTN4B76/events.json","paper":"https://pith.science/paper/4TJUA2R5"},"agent_actions":{"view_html":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76","download_json":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76.json","view_paper":"https://pith.science/paper/4TJUA2R5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.05762&json=true","fetch_graph":"https://pith.science/api/pith-number/4TJUA2R5W6YLVVDZYCFMTN4B76/graph.json","fetch_events":"https://pith.science/api/pith-number/4TJUA2R5W6YLVVDZYCFMTN4B76/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76/action/storage_attestation","attest_author":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76/action/author_attestation","sign_citation":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76/action/citation_signature","submit_replication":"https://pith.science/pith/4TJUA2R5W6YLVVDZYCFMTN4B76/action/replication_record"}},"created_at":"2026-07-05T07:22:32.431085+00:00","updated_at":"2026-07-05T07:22:32.431085+00:00"}