{"as_of":"2026-08-07T06:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1f4bb83113df9cb47e53c6d8f3cd83829b803f0e0c6160e8a8afd9168d546214","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:43:53.729012Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":65,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2305.18593","last_updated":"2025-03-25T03:01:44Z","snapshot_observed_at":"2026-08-03T17:02:54.684115Z","submitted_at":"2023-05-29T20:19:45Z","title":"On Diffusion Modeling for Anomaly Detection","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-24T08:51:14.163621Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2305.18593"},"observation_digest":"sha256:33bfba06835c93968ee55271ff35a7237fcc5a8ec7ba7e8042bb8de2a002a7e1","observation_id":"dd8e2585-ee4f-4c11-94ca-6fc71f5451ef","resolution":{"observed_at":"2026-05-24T08:54:15.959433Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-06T17:43:53.729012Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10088","last_updated":"2025-07-14T09:15:22Z","snapshot_observed_at":"2026-08-06T17:37:25.079459Z","submitted_at":"2025-07-14T09:15:22Z","title":"Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:43:53.729012Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2507.10088"},"observation_digest":"sha256:40a99cfc2db491fb97188fecd4142609e46bdcac93f772b76784a359b1180456","observation_id":"a21e7800-5d77-4875-b749-55040d54b546","resolution":{"observed_at":"2026-08-06T17:43:53.729012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-06T15:00:06.639190Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models, September 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17161","last_updated":"2025-07-23T02:53:58Z","snapshot_observed_at":"2026-08-06T14:53:21.376322Z","submitted_at":"2025-07-23T02:53:58Z","title":"Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T15:00:06.639190Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2507.17161"},"observation_digest":"sha256:076e7f3d6a63f1fbc19e10e120922a6c9f605e3a5109d605becd763713f44cdc","observation_id":"57145dd4-377e-4a3f-a18f-0da9db1cffe4","resolution":{"observed_at":"2026-08-06T15:00:06.639190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T11:32:57.940159Z","title":"arXiv:2209.15421 [cs.LG]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.05350","last_updated":"2025-09-02T18:44:07Z","snapshot_observed_at":"2026-08-05T11:32:55.128562Z","submitted_at":"2025-09-02T18:44:07Z","title":"Ensembling Membership Inference Attacks Against Tabular Generative Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T11:32:57.940159Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2509.05350"},"observation_digest":"sha256:7cb85c6abfcfaced58bd22a026223e43946525cb93965795b10286e7d11c7841","observation_id":"d4a1f90c-3864-410e-b44d-a0ce93e93210","resolution":{"observed_at":"2026-08-05T11:32:57.940159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2512.08875","last_updated":"2026-05-08T19:19:07Z","snapshot_observed_at":"2026-07-06T22:38:13.359449Z","submitted_at":"2025-12-09T18:06:31Z","title":"When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-16T23:47:31.667427Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2512.08875"},"observation_digest":"sha256:0eff5752859607fbe161363ebe9c67447668e830b8302772875468e22c0843d3","observation_id":"26f318e0-0b7a-43ca-b421-11ab922fb115","resolution":{"observed_at":"2026-05-16T23:48:41.889994Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2604.05257","last_updated":"2026-04-06T23:46:40Z","snapshot_observed_at":"2026-08-02T15:58:38.958758Z","submitted_at":"2026-04-06T23:46:40Z","title":"Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T18:42:28.942657Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2604.05257"},"observation_digest":"sha256:ba9941be983811dca31f77f8a1d194556368ec554b3aaaa8c301fee65a5fc062","observation_id":"14936023-aff9-438c-bf3c-0bfc2d5b272d","resolution":{"observed_at":"2026-05-11T00:05:50.241617Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2604.16817","last_updated":"2026-04-26T13:01:08Z","snapshot_observed_at":"2026-07-06T23:04:01.558812Z","submitted_at":"2026-04-18T04:15:24Z","title":"Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T06:56:54.006929Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2604.16817"},"observation_digest":"sha256:e1c4bb4e026d2a13418e3342273ec9e67a673a5cfc4d645675e5028e8e943f6e","observation_id":"30321f66-9c5d-4282-9a2c-af2990da754d","resolution":{"observed_at":"2026-05-10T07:01:49.532848Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2604.22688","last_updated":"2026-04-27T02:54:19Z","snapshot_observed_at":"2026-07-06T23:09:01.221922Z","submitted_at":"2026-04-24T16:24:05Z","title":"COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC","version":2},"reference_index":161,"source":"pdf_text","source_observed_at":"2026-05-08T08:53:25.483782Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2604.22688"},"observation_digest":"sha256:e40da551b422a57e30bbc3312da65eadfe3dcd70ee3294949179608585422aeb","observation_id":"85c14c4c-7fd9-4969-b77d-42806b413fc7","resolution":{"observed_at":"2026-05-08T22:29:19.441347Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2606.03251","last_updated":"2026-06-02T07:12:30Z","snapshot_observed_at":"2026-07-06T23:43:31.629075Z","submitted_at":"2026-06-02T07:12:30Z","title":"Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-28T09:57:53.889935Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2606.03251"},"observation_digest":"sha256:d946e1b1d6acd7c8f5819c3125bac090ffe05a256c4a68293449cc564e34eab6","observation_id":"8e026725-7c7d-4366-b4ae-102c7d04261e","resolution":{"observed_at":"2026-06-28T10:01:52.555175Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":"2209.15421","doi":"10.48550/arxiv.2209.15421","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":"arXiv (Cornell University)","work_id":"bbb94293-328e-43e6-9b72-3115ea9d9a7c","year":2024},"citing_paper":{"arxiv_id":"2606.31904","last_updated":"2026-06-30T16:09:10Z","snapshot_observed_at":"2026-07-07T00:05:31.888697Z","submitted_at":"2026-06-30T16:09:10Z","title":"Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-01T06:35:06.822132Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2606.31904"},"observation_digest":"sha256:010c1adb8e68e830db283759de56c879bd9d08cf6289f25050c8241922422233","observation_id":"31d20260-759e-4de4-86bf-c093a87cfc16","resolution":{"observed_at":"2026-07-01T06:35:29.341556Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-07-11T19:48:08.010459Z","title":"Kotelnikov, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04360","last_updated":"2026-07-05T15:38:35Z","snapshot_observed_at":"2026-08-06T06:26:51.249305Z","submitted_at":"2026-07-05T15:38:35Z","title":"Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-11T19:48:08.010459Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2607.04360"},"observation_digest":"sha256:8e324d3a5f67afd4812a9e582174a6d20a678ad9924b3d3fa806b4b35cdb25ee","observation_id":"599c1c1e-d10b-4e24-a81a-a15b662f9d5d","resolution":{"observed_at":"2026-07-11T19:48:08.010459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15421","snapshot_observed_at":"2026-08-03T16:50:36.973194Z","title":"TabDDPM : Modelling tabular data with diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28945","last_updated":"2026-07-31T01:57:07Z","snapshot_observed_at":"2026-08-05T23:12:09.495581Z","submitted_at":"2026-07-31T01:57:07Z","title":"FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-03T16:50:36.973194Z"},"links":{"cited_paper":"/paper/2209.15421","citing_paper":"/paper/2607.28945"},"observation_digest":"sha256:ff93297f81bc8f84f857a09ec19be13dbb6f870e8372ce837ae34d595d0792c1","observation_id":"4380d9e2-39b2-4e65-8b18-de7bed1e2353","resolution":{"observed_at":"2026-08-03T16:50:36.973194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2209.15421/citation-record","integrity":"/paper/2209.15421/integrity","json":"/paper/2209.15421/citation-record.json","paper":"/paper/2209.15421"},"outbound":[],"paper":{"arxiv_id":"2209.15421","last_updated":"2024-10-07T12:38:57Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T13:58:14.551432Z","submitted_at":"2022-09-30T12:26:14Z","title":"TabDDPM: Modelling Tabular Data with Diffusion Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2209.15421."}