{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3BA6XRGARV4X4RD3ECRMHSF3KP","short_pith_number":"pith:3BA6XRGA","canonical_record":{"source":{"id":"2305.09696","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-16T06:37:38Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7cb4be7e7808d2fb838094904efef5c9256e6685c38ece9d07a21f13508a831e","abstract_canon_sha256":"ce396ce91798b5ef6de34aac8c5c02eadeffb147dfb39f6ef1180afd04716e51"},"schema_version":"1.0"},"canonical_sha256":"d841ebc4c08d797e447b20a2c3c8bb53d07585eb39df9a7670844013bcd41e58","source":{"kind":"arxiv","id":"2305.09696","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.09696","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"arxiv_version","alias_value":"2305.09696v1","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.09696","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"pith_short_12","alias_value":"3BA6XRGARV4X","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"pith_short_16","alias_value":"3BA6XRGARV4X4RD3","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"pith_short_8","alias_value":"3BA6XRGA","created_at":"2026-07-05T06:10:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3BA6XRGARV4X4RD3ECRMHSF3KP","target":"record","payload":{"canonical_record":{"source":{"id":"2305.09696","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-16T06:37:38Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7cb4be7e7808d2fb838094904efef5c9256e6685c38ece9d07a21f13508a831e","abstract_canon_sha256":"ce396ce91798b5ef6de34aac8c5c02eadeffb147dfb39f6ef1180afd04716e51"},"schema_version":"1.0"},"canonical_sha256":"d841ebc4c08d797e447b20a2c3c8bb53d07585eb39df9a7670844013bcd41e58","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:10:52.915482Z","signature_b64":"CuU/LUdzjGgzLlF1FWMnmlEu8Bwcilhy1iMofEex8es6dJyyC6cj/OWMwDOjRWbHWDRPZC+qDkaTvAzvylOnBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d841ebc4c08d797e447b20a2c3c8bb53d07585eb39df9a7670844013bcd41e58","last_reissued_at":"2026-07-05T06:10:52.915070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:10:52.915070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.09696","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":"2026-07-05T06:10:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0yBLEEeiz4bGIXFOe9IdftBd6kBpsnxMZASe0cKjBln/dvR2yU0GnLg7/4EL1fmbo8Vqn3BlCwfjFbQ/13dXCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T13:46:00.488241Z"},"content_sha256":"5a198bb74d38d05c135f823d25cdc8666056a8ac04e226c95c65e7d0fbfb6417","schema_version":"1.0","event_id":"sha256:5a198bb74d38d05c135f823d25cdc8666056a8ac04e226c95c65e7d0fbfb6417"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3BA6XRGARV4X4RD3ECRMHSF3KP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Table Pre-training Empowers Models for Tabular Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Jian Li, Qian Liu, Shaowen Wang, Shuicheng Yan, Tianping Zhang","submitted_at":"2023-05-16T06:37:38Z","abstract_excerpt":"Recently, the topic of table pre-training has attracted considerable research interest. However, how to employ table pre-training to boost the performance of tabular prediction remains an open challenge. In this paper, we propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. After pre-training on a large corpus of real-world tabular data, TapTap can generate high-quality synthetic tables to support various applications on tabular data, including privacy protection, low resource regime, missing value imputation, and imbalanced classificati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.09696","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/2305.09696/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":"2026-07-05T06:10:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T5LGb/QURZxadTgfMrgg29bRXVHhWUIxukOoNFWAP5EviTrVYTd18NlwP7RZi/2r4zdx4xclj4i3kL3f7Df/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T13:46:00.488635Z"},"content_sha256":"f1ceac9cea9099fbdd865b7e031a6d80525d1f88562291f78de2c5c4bcb09a81","schema_version":"1.0","event_id":"sha256:f1ceac9cea9099fbdd865b7e031a6d80525d1f88562291f78de2c5c4bcb09a81"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3BA6XRGARV4X4RD3ECRMHSF3KP/bundle.json","state_url":"https://pith.science/pith/3BA6XRGARV4X4RD3ECRMHSF3KP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3BA6XRGARV4X4RD3ECRMHSF3KP/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":"2026-08-02T13:46:00Z","links":{"resolver":"https://pith.science/pith/3BA6XRGARV4X4RD3ECRMHSF3KP","bundle":"https://pith.science/pith/3BA6XRGARV4X4RD3ECRMHSF3KP/bundle.json","state":"https://pith.science/pith/3BA6XRGARV4X4RD3ECRMHSF3KP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3BA6XRGARV4X4RD3ECRMHSF3KP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3BA6XRGARV4X4RD3ECRMHSF3KP","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":"ce396ce91798b5ef6de34aac8c5c02eadeffb147dfb39f6ef1180afd04716e51","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-16T06:37:38Z","title_canon_sha256":"7cb4be7e7808d2fb838094904efef5c9256e6685c38ece9d07a21f13508a831e"},"schema_version":"1.0","source":{"id":"2305.09696","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.09696","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"arxiv_version","alias_value":"2305.09696v1","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.09696","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"pith_short_12","alias_value":"3BA6XRGARV4X","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"pith_short_16","alias_value":"3BA6XRGARV4X4RD3","created_at":"2026-07-05T06:10:52Z"},{"alias_kind":"pith_short_8","alias_value":"3BA6XRGA","created_at":"2026-07-05T06:10:52Z"}],"graph_snapshots":[{"event_id":"sha256:f1ceac9cea9099fbdd865b7e031a6d80525d1f88562291f78de2c5c4bcb09a81","target":"graph","created_at":"2026-07-05T06:10:52Z","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/2305.09696/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, the topic of table pre-training has attracted considerable research interest. However, how to employ table pre-training to boost the performance of tabular prediction remains an open challenge. In this paper, we propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. After pre-training on a large corpus of real-world tabular data, TapTap can generate high-quality synthetic tables to support various applications on tabular data, including privacy protection, low resource regime, missing value imputation, and imbalanced classificati","authors_text":"Jian Li, Qian Liu, Shaowen Wang, Shuicheng Yan, Tianping Zhang","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-16T06:37:38Z","title":"Generative Table Pre-training Empowers Models for Tabular Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.09696","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":"sha256:5a198bb74d38d05c135f823d25cdc8666056a8ac04e226c95c65e7d0fbfb6417","target":"record","created_at":"2026-07-05T06:10:52Z","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":"ce396ce91798b5ef6de34aac8c5c02eadeffb147dfb39f6ef1180afd04716e51","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-16T06:37:38Z","title_canon_sha256":"7cb4be7e7808d2fb838094904efef5c9256e6685c38ece9d07a21f13508a831e"},"schema_version":"1.0","source":{"id":"2305.09696","kind":"arxiv","version":1}},"canonical_sha256":"d841ebc4c08d797e447b20a2c3c8bb53d07585eb39df9a7670844013bcd41e58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d841ebc4c08d797e447b20a2c3c8bb53d07585eb39df9a7670844013bcd41e58","first_computed_at":"2026-07-05T06:10:52.915070Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:10:52.915070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CuU/LUdzjGgzLlF1FWMnmlEu8Bwcilhy1iMofEex8es6dJyyC6cj/OWMwDOjRWbHWDRPZC+qDkaTvAzvylOnBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:10:52.915482Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.09696","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5a198bb74d38d05c135f823d25cdc8666056a8ac04e226c95c65e7d0fbfb6417","sha256:f1ceac9cea9099fbdd865b7e031a6d80525d1f88562291f78de2c5c4bcb09a81"],"state_sha256":"404ed76ec6ac3781f5325924583ef63bd6545a56b6c18e694cb28e9c90f8d71a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"62Mxmiyy9nBsSfoAZYvoDWLxPZxK6pzy7X9rtoExI9Vab2eqbpIgdqrgrOTV5XjM7tPfqGqBp68xSgHw21OUBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T13:46:00.491597Z","bundle_sha256":"68feb93d90e549ace593bc8e010d9f2a94cf70400be23dbda396e073062291f5"}}