{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JQP7BMDPA4G3PDSIHLZIC6VC3D","short_pith_number":"pith:JQP7BMDP","schema_version":"1.0","canonical_sha256":"4c1ff0b06f070db78e483af2817aa2d8e89f770536d1aba866f202b7c56d08eb","source":{"kind":"arxiv","id":"2506.14206","version":1},"attestation_state":"computed","paper":{"title":"CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chun-Ming Xia, Fei Dai, Jia-Chen Zhang, Yu-Jie Xiong, Zheng Zhou","submitted_at":"2025-06-17T05:48:44Z","abstract_excerpt":"Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issues presenting a significant hurdle. To address the need for high-quality data. Synthesize data has emerged as a mainstream solution, demonstrating impressive performance in areas such as images, audio, and video. Generating mixed-type data, especially high-quality tabular data, still faces significant challenges. These primarily include its inherent heterogeneous data types, complex inter-variable relationships, and i"},"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":"2506.14206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-17T05:48:44Z","cross_cats_sorted":[],"title_canon_sha256":"eb40ce32a930eb4c14fc381034c2838eac82c43d20ad92292d86203fb0c460e0","abstract_canon_sha256":"8e7f5f5d36769211949f46780ddb26233a84629dd6bbadfb2e2e905536fd1b12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:50.856475Z","signature_b64":"EtM6cmGOx+bUqMUFz5BzmkNXIeAXc8vxmvDA9aDB/iFny+zNg3j/Zsk1psYtd4x9CriLJswjGaTuz+EavzFRCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c1ff0b06f070db78e483af2817aa2d8e89f770536d1aba866f202b7c56d08eb","last_reissued_at":"2026-07-05T11:22:50.855971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:50.855971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chun-Ming Xia, Fei Dai, Jia-Chen Zhang, Yu-Jie Xiong, Zheng Zhou","submitted_at":"2025-06-17T05:48:44Z","abstract_excerpt":"Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issues presenting a significant hurdle. To address the need for high-quality data. Synthesize data has emerged as a mainstream solution, demonstrating impressive performance in areas such as images, audio, and video. Generating mixed-type data, especially high-quality tabular data, still faces significant challenges. These primarily include its inherent heterogeneous data types, complex inter-variable relationships, and i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14206","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/2506.14206/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":"2506.14206","created_at":"2026-07-05T11:22:50.856030+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14206v1","created_at":"2026-07-05T11:22:50.856030+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14206","created_at":"2026-07-05T11:22:50.856030+00:00"},{"alias_kind":"pith_short_12","alias_value":"JQP7BMDPA4G3","created_at":"2026-07-05T11:22:50.856030+00:00"},{"alias_kind":"pith_short_16","alias_value":"JQP7BMDPA4G3PDSI","created_at":"2026-07-05T11:22:50.856030+00:00"},{"alias_kind":"pith_short_8","alias_value":"JQP7BMDP","created_at":"2026-07-05T11:22:50.856030+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D","json":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D.json","graph_json":"https://pith.science/api/pith-number/JQP7BMDPA4G3PDSIHLZIC6VC3D/graph.json","events_json":"https://pith.science/api/pith-number/JQP7BMDPA4G3PDSIHLZIC6VC3D/events.json","paper":"https://pith.science/paper/JQP7BMDP"},"agent_actions":{"view_html":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D","download_json":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D.json","view_paper":"https://pith.science/paper/JQP7BMDP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14206&json=true","fetch_graph":"https://pith.science/api/pith-number/JQP7BMDPA4G3PDSIHLZIC6VC3D/graph.json","fetch_events":"https://pith.science/api/pith-number/JQP7BMDPA4G3PDSIHLZIC6VC3D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D/action/storage_attestation","attest_author":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D/action/author_attestation","sign_citation":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D/action/citation_signature","submit_replication":"https://pith.science/pith/JQP7BMDPA4G3PDSIHLZIC6VC3D/action/replication_record"}},"created_at":"2026-07-05T11:22:50.856030+00:00","updated_at":"2026-07-05T11:22:50.856030+00:00"}