{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:Q3TGNU2JLQ23TJQW75MWTHDK3M","short_pith_number":"pith:Q3TGNU2J","canonical_record":{"source":{"id":"2503.20903","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-26T18:19:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"32053d34c35522daddecad88a860bdf91ffcf4ce8f5bedc89e6d43013905ce8c","abstract_canon_sha256":"aa693d7d5f382def98f2cf0ed0c91de9270fc064893c26c280b9af7fd9f1a09a"},"schema_version":"1.0"},"canonical_sha256":"86e666d3495c35b9a616ff59699c6adb26cf82e4f2eef49ba859adbf1e25271f","source":{"kind":"arxiv","id":"2503.20903","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.20903","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"arxiv_version","alias_value":"2503.20903v1","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.20903","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"pith_short_12","alias_value":"Q3TGNU2JLQ23","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"pith_short_16","alias_value":"Q3TGNU2JLQ23TJQW","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"pith_short_8","alias_value":"Q3TGNU2J","created_at":"2026-07-05T10:39:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:Q3TGNU2JLQ23TJQW75MWTHDK3M","target":"record","payload":{"canonical_record":{"source":{"id":"2503.20903","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-26T18:19:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"32053d34c35522daddecad88a860bdf91ffcf4ce8f5bedc89e6d43013905ce8c","abstract_canon_sha256":"aa693d7d5f382def98f2cf0ed0c91de9270fc064893c26c280b9af7fd9f1a09a"},"schema_version":"1.0"},"canonical_sha256":"86e666d3495c35b9a616ff59699c6adb26cf82e4f2eef49ba859adbf1e25271f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:39:50.111080Z","signature_b64":"gbDoshTYzvmVboapWvv0ErodJafWI6eoz/zQMBfx+xuoV6uarAKzYe0/0LdgEjAyMWWBSzOxzkw7LXPo12P2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86e666d3495c35b9a616ff59699c6adb26cf82e4f2eef49ba859adbf1e25271f","last_reissued_at":"2026-07-05T10:39:50.110668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:39:50.110668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.20903","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":"2026-07-05T10:39:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HCgBF5P6Nm/HYridxSk2Ig27k3r7xgfqsjazOhM30T/JwWhVRlDisNjH8HT38FJwf3Y2n4NZt9xSKr/kSWigCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T16:52:43.643004Z"},"content_sha256":"34fcad43967f87b86b64faa361bf444f8b99c12bd884f1a58c7f7a17fd1d6319","schema_version":"1.0","event_id":"sha256:34fcad43967f87b86b64faa361bf444f8b99c12bd884f1a58c7f7a17fd1d6319"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:Q3TGNU2JLQ23TJQW75MWTHDK3M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Assessing Generative Models for Structured Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amy Wagler, Andy Lin, Caesar Vazquez, Nicolette M. Laird, Reilly Cannon, Tony Chiang","submitted_at":"2025-03-26T18:19:05Z","abstract_excerpt":"Synthetic tabular data generation has emerged as a promising method to address limited data availability and privacy concerns. With the sharp increase in the performance of large language models in recent years, researchers have been interested in applying these models to the generation of tabular data. However, little is known about the quality of the generated tabular data from large language models. The predominant method for assessing the quality of synthetic tabular data is the train-synthetic-test-real approach, where the artificial examples are compared to the original by how well machi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.20903","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/2503.20903/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":"2026-07-05T10:39:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4BaFdLlUnLzS+LzUplsoB/KKzso45gjqVfMMwqsNj2zG1/sRChdawQ4rpa3DmM8zCcTfLWFxiyodkyalaxWYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T16:52:43.643705Z"},"content_sha256":"9974f4f7957edda5d9f0262df452d2ff4c0bcab60dd096182c483962477afb9c","schema_version":"1.0","event_id":"sha256:9974f4f7957edda5d9f0262df452d2ff4c0bcab60dd096182c483962477afb9c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M/bundle.json","state_url":"https://pith.science/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M/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":"2026-08-10T16:52:43Z","links":{"resolver":"https://pith.science/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M","bundle":"https://pith.science/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M/bundle.json","state":"https://pith.science/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q3TGNU2JLQ23TJQW75MWTHDK3M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Q3TGNU2JLQ23TJQW75MWTHDK3M","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":"aa693d7d5f382def98f2cf0ed0c91de9270fc064893c26c280b9af7fd9f1a09a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-26T18:19:05Z","title_canon_sha256":"32053d34c35522daddecad88a860bdf91ffcf4ce8f5bedc89e6d43013905ce8c"},"schema_version":"1.0","source":{"id":"2503.20903","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.20903","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"arxiv_version","alias_value":"2503.20903v1","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.20903","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"pith_short_12","alias_value":"Q3TGNU2JLQ23","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"pith_short_16","alias_value":"Q3TGNU2JLQ23TJQW","created_at":"2026-07-05T10:39:50Z"},{"alias_kind":"pith_short_8","alias_value":"Q3TGNU2J","created_at":"2026-07-05T10:39:50Z"}],"graph_snapshots":[{"event_id":"sha256:9974f4f7957edda5d9f0262df452d2ff4c0bcab60dd096182c483962477afb9c","target":"graph","created_at":"2026-07-05T10:39:50Z","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/2503.20903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Synthetic tabular data generation has emerged as a promising method to address limited data availability and privacy concerns. With the sharp increase in the performance of large language models in recent years, researchers have been interested in applying these models to the generation of tabular data. However, little is known about the quality of the generated tabular data from large language models. The predominant method for assessing the quality of synthetic tabular data is the train-synthetic-test-real approach, where the artificial examples are compared to the original by how well machi","authors_text":"Amy Wagler, Andy Lin, Caesar Vazquez, Nicolette M. Laird, Reilly Cannon, Tony Chiang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-26T18:19:05Z","title":"Assessing Generative Models for Structured Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.20903","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":"sha256:34fcad43967f87b86b64faa361bf444f8b99c12bd884f1a58c7f7a17fd1d6319","target":"record","created_at":"2026-07-05T10:39:50Z","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":"aa693d7d5f382def98f2cf0ed0c91de9270fc064893c26c280b9af7fd9f1a09a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-26T18:19:05Z","title_canon_sha256":"32053d34c35522daddecad88a860bdf91ffcf4ce8f5bedc89e6d43013905ce8c"},"schema_version":"1.0","source":{"id":"2503.20903","kind":"arxiv","version":1}},"canonical_sha256":"86e666d3495c35b9a616ff59699c6adb26cf82e4f2eef49ba859adbf1e25271f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86e666d3495c35b9a616ff59699c6adb26cf82e4f2eef49ba859adbf1e25271f","first_computed_at":"2026-07-05T10:39:50.110668Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:39:50.110668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gbDoshTYzvmVboapWvv0ErodJafWI6eoz/zQMBfx+xuoV6uarAKzYe0/0LdgEjAyMWWBSzOxzkw7LXPo12P2AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:39:50.111080Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.20903","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34fcad43967f87b86b64faa361bf444f8b99c12bd884f1a58c7f7a17fd1d6319","sha256:9974f4f7957edda5d9f0262df452d2ff4c0bcab60dd096182c483962477afb9c"],"state_sha256":"fa2bb327672a23e45ca448f1ed14fe5ce2f6d73ef87608eab1bf102e707277b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"If4DOOjbMyx6RmpBHsUjDPkZxpl7mSapyStuU3wLWsAEI6ATlcSLBprxlEoAkMlLibeYzQBz59IkTCGF6vXLBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T16:52:43.647448Z","bundle_sha256":"41fafad1649e80f6f1996066e8b320f427de9e54f7278958bb09521fb93c4672"}}