{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IV46SFRPF6LX2MXEKGNKCIBZVQ","short_pith_number":"pith:IV46SFRP","canonical_record":{"source":{"id":"2509.09969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-12T05:08:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e2bdcd3e8d648399b744c28310e5d22fdd6b54dcc2dc368691494a94c3ff811f","abstract_canon_sha256":"5afa5f33b67511c4ccea505374008ff765fb4c74eaddb8fb0f9160259b5b1875"},"schema_version":"1.0"},"canonical_sha256":"4579e9162f2f977d32e4519aa12039ac2347587bc96fc2828705862b8ebf8f61","source":{"kind":"arxiv","id":"2509.09969","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09969","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09969v1","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09969","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"IV46SFRPF6LX","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"IV46SFRPF6LX2MXE","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"IV46SFRP","created_at":"2026-07-05T12:09:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IV46SFRPF6LX2MXEKGNKCIBZVQ","target":"record","payload":{"canonical_record":{"source":{"id":"2509.09969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-12T05:08:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e2bdcd3e8d648399b744c28310e5d22fdd6b54dcc2dc368691494a94c3ff811f","abstract_canon_sha256":"5afa5f33b67511c4ccea505374008ff765fb4c74eaddb8fb0f9160259b5b1875"},"schema_version":"1.0"},"canonical_sha256":"4579e9162f2f977d32e4519aa12039ac2347587bc96fc2828705862b8ebf8f61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:52.944630Z","signature_b64":"powB9luUx4TxV3vxCjSyhh+rPJpHBsecYJfoMUoYP0b+lqgCpL4iJ3ZRXV3hwMpjZdyArIeiva82XB8h1cZhCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4579e9162f2f977d32e4519aa12039ac2347587bc96fc2828705862b8ebf8f61","last_reissued_at":"2026-07-05T12:09:52.944106Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:52.944106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.09969","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LnsTP2Xyw47IU5oAzUYCVG3RlLVkBeJswbiX5+P/LYm5jXY8a/7STDZXYr+4fCYGIoh8tSkFfcXAB6CGbVueDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:06:35.800351Z"},"content_sha256":"c9c0f0078bdbb1e4e7d60b012a7fd47cc3c74e3cdbc0b63643d0d5a7a4af542f","schema_version":"1.0","event_id":"sha256:c9c0f0078bdbb1e4e7d60b012a7fd47cc3c74e3cdbc0b63643d0d5a7a4af542f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IV46SFRPF6LX2MXEKGNKCIBZVQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models Meet Legal Artificial Intelligence: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kun Zeng, Nanli Zeng, Tianyong Hao, Zhitian Hou, Zihan Ye","submitted_at":"2025-09-12T05:08:11Z","abstract_excerpt":"Large Language Models (LLMs) have significantly advanced the development of Legal Artificial Intelligence (Legal AI) in recent years, enhancing the efficiency and accuracy of legal tasks. To advance research and applications of LLM-based approaches in legal domain, this paper provides a comprehensive review of 16 legal LLMs series and 47 LLM-based frameworks for legal tasks, and also gather 15 benchmarks and 29 datasets to evaluate different legal capabilities. Additionally, we analyse the challenges and discuss future directions for LLM-based approaches in the legal domain. We hope this paper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09969","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/2509.09969/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MNwUON94WjwdM3Wawv5A41Wtls5ZUvLsGxmFOwJUGbzjobPy6+kv3IiGofKlyK95vOMHRRjtOoSvdlZVb4QjBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:06:35.801442Z"},"content_sha256":"bfe1f3efaba281e67792af0241512b0ec566e72f2ddceb4103b14e08a34dcdf5","schema_version":"1.0","event_id":"sha256:bfe1f3efaba281e67792af0241512b0ec566e72f2ddceb4103b14e08a34dcdf5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ/bundle.json","state_url":"https://pith.science/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T01:06:35Z","links":{"resolver":"https://pith.science/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ","bundle":"https://pith.science/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ/bundle.json","state":"https://pith.science/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IV46SFRPF6LX2MXEKGNKCIBZVQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IV46SFRPF6LX2MXEKGNKCIBZVQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5afa5f33b67511c4ccea505374008ff765fb4c74eaddb8fb0f9160259b5b1875","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-12T05:08:11Z","title_canon_sha256":"e2bdcd3e8d648399b744c28310e5d22fdd6b54dcc2dc368691494a94c3ff811f"},"schema_version":"1.0","source":{"id":"2509.09969","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09969","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09969v1","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09969","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"IV46SFRPF6LX","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"IV46SFRPF6LX2MXE","created_at":"2026-07-05T12:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"IV46SFRP","created_at":"2026-07-05T12:09:52Z"}],"graph_snapshots":[{"event_id":"sha256:bfe1f3efaba281e67792af0241512b0ec566e72f2ddceb4103b14e08a34dcdf5","target":"graph","created_at":"2026-07-05T12:09:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.09969/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have significantly advanced the development of Legal Artificial Intelligence (Legal AI) in recent years, enhancing the efficiency and accuracy of legal tasks. To advance research and applications of LLM-based approaches in legal domain, this paper provides a comprehensive review of 16 legal LLMs series and 47 LLM-based frameworks for legal tasks, and also gather 15 benchmarks and 29 datasets to evaluate different legal capabilities. Additionally, we analyse the challenges and discuss future directions for LLM-based approaches in the legal domain. We hope this paper","authors_text":"Kun Zeng, Nanli Zeng, Tianyong Hao, Zhitian Hou, Zihan Ye","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-12T05:08:11Z","title":"Large Language Models Meet Legal Artificial Intelligence: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09969","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c9c0f0078bdbb1e4e7d60b012a7fd47cc3c74e3cdbc0b63643d0d5a7a4af542f","target":"record","created_at":"2026-07-05T12:09:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5afa5f33b67511c4ccea505374008ff765fb4c74eaddb8fb0f9160259b5b1875","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-12T05:08:11Z","title_canon_sha256":"e2bdcd3e8d648399b744c28310e5d22fdd6b54dcc2dc368691494a94c3ff811f"},"schema_version":"1.0","source":{"id":"2509.09969","kind":"arxiv","version":1}},"canonical_sha256":"4579e9162f2f977d32e4519aa12039ac2347587bc96fc2828705862b8ebf8f61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4579e9162f2f977d32e4519aa12039ac2347587bc96fc2828705862b8ebf8f61","first_computed_at":"2026-07-05T12:09:52.944106Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:52.944106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"powB9luUx4TxV3vxCjSyhh+rPJpHBsecYJfoMUoYP0b+lqgCpL4iJ3ZRXV3hwMpjZdyArIeiva82XB8h1cZhCA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:52.944630Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09969","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9c0f0078bdbb1e4e7d60b012a7fd47cc3c74e3cdbc0b63643d0d5a7a4af542f","sha256:bfe1f3efaba281e67792af0241512b0ec566e72f2ddceb4103b14e08a34dcdf5"],"state_sha256":"7d4dd38792aa2be89a2c158f0fb39d4ca70640b081a5721e462274ba0740b7f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rwADPanVfw9gNKXZ5vwOTmB1L8aum5L4Wh5eyY5f8YHEg0ZP6HOTbp6h4Nc7BvpwvpA65UMJuUa7kNEUd718Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T01:06:35.822687Z","bundle_sha256":"daaf342740f33d46710905af43c1c0a75888fc57d37a4c1e37e2bdb046df0cf8"}}