{"as_of":"2026-08-09T02:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1e262010b2e3aadd7d7eb72eeb1207383205902b8b9bd2a130674838001dbe86","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T10:38:14.264744Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T17:36:45.173113Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T06:31:00.923980Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"cited_work":{"arxiv_id":"2510.09783","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2510.09783","snapshot_observed_at":"2026-06-09T03:07:04.959484Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","venue":null,"work_id":"881cc2fb-75bf-4700-9279-f14a65ac2a18","year":2025},"citing_paper":{"arxiv_id":"2604.08628","last_updated":"2026-04-09T16:13:03Z","snapshot_observed_at":"2026-08-03T18:17:01.293302Z","submitted_at":"2026-04-09T16:13:03Z","title":"Retrieval Augmented Classification for Confidential Documents","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T17:36:45.173113Z"},"links":{"cited_paper":"/paper/2510.09783","citing_paper":"/paper/2604.08628"},"observation_digest":"sha256:a4916a71249f02fdaf81013e6fb7d7b21f6da2343ae49f5fb7935ed0346b3f06","observation_id":"a46915b6-6188-49b7-805a-94d20a74b67a","resolution":{"observed_at":"2026-06-09T03:07:04.959484Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2510.09783/citation-record","integrity":"/paper/2510.09783/integrity","json":"/paper/2510.09783/citation-record.json","paper":"/paper/2510.09783"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:10.317839Z","title":"Generating synthetic data in finance: opportunities, challenges and pitfalls","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:10.317839Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:736fc371273d92f3339507213997d864319bb63c1acfce5b4bbcf7c98b1f386a","observation_id":"1687af39-92ba-4d81-be6f-6bad57774e17","resolution":{"observed_at":"2026-08-04T10:38:10.317839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:10.474775Z","title":"Table-to-text: Describing table region with natural language","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:10.474775Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:674920252b7678246ff7865ab8e4931fbdc314b07be0473dc13f635db68d1bb4","observation_id":"3278013c-6b44-4014-aab8-fdec99b722a9","resolution":{"observed_at":"2026-08-04T10:38:10.474775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:10.550564Z","title":"SciBERT: A Pretrained Language Model for Scientific Text","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:10.550564Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:ef258043ce98224a4e705df09ba5078b8b250b5e762c0f90c16d5940c37db734","observation_id":"a231af2e-9d84-4b23-8098-7c810370e34b","resolution":{"observed_at":"2026-08-04T10:38:10.550564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:10.611293Z","title":"Deep neural networks and tabular data: A survey.IEEE Transactions on Neural Networks and Learning Systems, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:10.611293Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:258701feef768ceaa308760bf3ae44bdb975ec5c6d9159fc82db0bc73ada1417","observation_id":"e27e6860-16ae-4a96-a08c-ef4471688413","resolution":{"observed_at":"2026-08-04T10:38:10.611293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:10.723289Z","title":"Language models are realistic tabular data generators","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:10.723289Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:fc72f931895c9155e1f90e3cc93a28595c96f29adf2e16b54f6d8a71f9eff4ab","observation_id":"aaf6a4d9-7e5e-45f1-a2e0-43cde536ce4e","resolution":{"observed_at":"2026-08-04T10:38:10.723289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:10.867377Z","title":"Learning imbalanced datasets with label-distribution-aware margin loss","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:10.867377Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:39dfe49739fa57f6ff57002a7743f984f5a2e1881d96840bdb7a680842c3b8b2","observation_id":"393c69d6-1390-432f-8b22-655d9a9121f9","resolution":{"observed_at":"2026-08-04T10:38:10.867377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.025247Z","title":"SMOTE: synthetic minority over-sampling technique.Journal of Artifi- cial Intelligence Research, 2002","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.025247Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:25ddbd0ca8022c8f9789248720e1a0c6b08c84e267f671a37496e5006f114f47","observation_id":"87ebe2c1-6fa2-4480-bc9b-03a0ece0190f","resolution":{"observed_at":"2026-08-04T10:38:11.025247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.053000Z","title":"Xgboost: A scalable tree boosting system","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.053000Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:016a78f59e98da9a1923cf67d47b8be0cc2130b0c322f8f31c2074a6b9237be3","observation_id":"09b170f6-f825-40e8-bc62-1467fd32081f","resolution":{"observed_at":"2026-08-04T10:38:11.053000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.224936Z","title":"TabFact : A Large- scale Dataset for Table-based Fact Verification","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.224936Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:7c84c2b6ba01d47c219308ba17323e30ab19dabf5a0809771a69fd8c175cefe0","observation_id":"525d8356-abad-4f24-a4c7-d9c617af9d89","resolution":{"observed_at":"2026-08-04T10:38:11.224936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.373691Z","title":"Class-balanced loss based on effective number of samples","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.373691Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:9c337fe9db311e42bae86cca78408b08cb3cea04a1416c96582a85ab99b0fbde","observation_id":"31561a2f-8958-497d-9b24-f19f21a6d0f3","resolution":{"observed_at":"2026-08-04T10:38:11.373691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.486989Z","title":"Generative Adversarial Nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.486989Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:a40232226b0c8c55302cf5db4a7ec73dd42586b8939c361cd08c57eb35524308","observation_id":"b8056b0d-aa12-4818-87b7-594b8c668a00","resolution":{"observed_at":"2026-08-04T10:38:11.486989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08815","last_updated":"2022-07-18T08:36:08Z","snapshot_observed_at":"2026-08-04T06:27:22.738026Z","submitted_at":"2022-07-18T08:36:08Z","title":"Why do tree-based models still outperform deep learning on tabular data?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.08815","snapshot_observed_at":"2026-08-04T10:38:11.555708Z","title":"Why do tree- based models still outperform deep learning on tabular data?arXiv preprint arXiv:2207.08815, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.555708Z"},"links":{"cited_paper":"/paper/2207.08815","citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:9143119d8130b86f4191b7c8ecf33227064a1e46da651231ec1d8ea73b7a8964","observation_id":"d0f16e19-690b-4b85-924c-ea1196ffa371","resolution":{"observed_at":"2026-08-04T10:38:11.555708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.624364Z","title":"Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.624364Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:f3cdfad57ad48d50b893530874a3f2ee0cad6a61c38ba1fce013a851927f2dd4","observation_id":"29af3126-acdb-4a96-a901-b245b3a970bd","resolution":{"observed_at":"2026-08-04T10:38:11.624364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.690992Z","title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.690992Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:c12bfc7ac0c582502b7f900b6e64038e86004f1867179d8bb16a5e4be2721507","observation_id":"51a07cad-dc0a-4f25-a45f-0825dd2c8508","resolution":{"observed_at":"2026-08-04T10:38:11.690992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:11.857716Z","title":"Learning from imbalanced data.IEEE Transactions on knowledge and data engineering, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:11.857716Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:1fa04f354b767c8b82207f0e043c9b1d6bf5ceac5fdc96122a479a3ed002e153","observation_id":"97c6bba2-d397-4df5-9463-045994746446","resolution":{"observed_at":"2026-08-04T10:38:11.857716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.064073Z","title":"Tabllm: Few-shot classification of tabular data with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.064073Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:38dc8daf5a43c0a90925384d04f213a47d6f942eb16d79fee105ddcd261b7576","observation_id":"ef3e056b-5c3b-49a6-b45e-53c583fa0d42","resolution":{"observed_at":"2026-08-04T10:38:12.064073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.180448Z","title":"TaPas: Weakly Supervised Table Parsing via Pre-training","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.180448Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:c25d1e8f31c7c50c582f7c3c084134873d6190e7abd05d472786d6215a296293","observation_id":"77e377ba-cc2b-47fa-abca-a78328b11286","resolution":{"observed_at":"2026-08-04T10:38:12.180448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.309173Z","title":"Oct-GAN: Neural ODE-based conditional tabular GANs","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.309173Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:a5f933d6f43f763dbf5311c83604828653382f040d633ba85caec9af400586de","observation_id":"26120b37-0fab-42eb-ba43-73d72b59c00f","resolution":{"observed_at":"2026-08-04T10:38:12.309173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.431241Z","title":"An introduction to variational autoencoders.Foundations and Trends in Machine Learning, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.431241Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:d27863f6e8cfdb11209ddf781ebb7d4e897d46f6884cc468f0e4301c5ff15ecd","observation_id":"af13d8e3-cf85-4a3f-9614-171e99d5a64c","resolution":{"observed_at":"2026-08-04T10:38:12.431241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.587572Z","title":"Reliable fidelity and diversity metrics for generative models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.587572Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:20e3d265b0e1e92f3521ebf04fe4f21da91938abb2efb92aae37c38ec86f195e","observation_id":"c8c9a898-c81b-404f-bfcd-df7fce3ba667","resolution":{"observed_at":"2026-08-04T10:38:12.587572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.716070Z","title":"Generating realistic tabular data with large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.716070Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:85e2d7cfdff08988edb59c6d9becccb5f8e84eddf8171798848885b08116502e","observation_id":"78487a1a-7643-4389-8181-3a74f631ce7c","resolution":{"observed_at":"2026-08-04T10:38:12.716070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:12.872197Z","title":"Fairness Improvement for Black-box Classifiers with Gaus- sian Process.Information Sciences, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:12.872197Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:5a8e2b71dc0907a1b22df60f81cec90b1bc423726ea57c5eb8ae904e3e2f2de1","observation_id":"9b6d1986-e901-44fc-98e8-0adbe09b53d2","resolution":{"observed_at":"2026-08-04T10:38:12.872197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.004282Z","title":"Data Synthesis Based on Generative Adversarial Networks.Proceedings of the VLDB Endowment, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.004282Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:6a1128e027841d3ffef2efd7592044bf3ee1256be9414bc95d4194543cd9bb66","observation_id":"297732de-d314-4fe4-9657-0f9dec564e59","resolution":{"observed_at":"2026-08-04T10:38:13.004282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.07573","last_updated":"2023-01-18T14:49:54Z","snapshot_observed_at":"2026-07-06T14:42:30.096301Z","submitted_at":"2023-01-18T14:49:54Z","title":"Synthcity: facilitating innovative use cases of synthetic data in different data modalities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.07573","snapshot_observed_at":"2026-08-04T10:38:13.139067Z","title":"Synthcity: facilitating innovative use cases of synthetic data in different data modalities.arXiv preprint arXiv:2301.07573, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.139067Z"},"links":{"cited_paper":"/paper/2301.07573","citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:4249919c0b186cbfc5caa56a2cc20dc898757e5fc1735ac0fae3b0fcae2c6835","observation_id":"8a096402-9da5-4f95-bc2e-9262fe4b1596","resolution":{"observed_at":"2026-08-04T10:38:13.139067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.278897Z","title":"A novel SMOTE-based re- sampling technique trough noise detection and the boosting procedure","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.278897Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:cb2cf69756b06750f727999fbd014119b2f24767e1d7f027c6c2ea91ad6b4b4b","observation_id":"c2abd5fd-5707-4472-82ab-081cc0c617f3","resolution":{"observed_at":"2026-08-04T10:38:13.278897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.343876Z","title":"Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.343876Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:0c52869595b366c61ab322948672cc59ffee17ceb890a2ae25fd015de3838ea2","observation_id":"41fbf94c-1234-4fa9-b3e6-1fe92ac7cf15","resolution":{"observed_at":"2026-08-04T10:38:13.343876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.468471Z","title":"Tabular data: Deep learning is not all you need.Information Fusion, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.468471Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:736e5c37e5db3ff0a0e0904deb3ac11d4bea85418b4a96fdb54fb0043202255c","observation_id":"85bd77d6-1c9c-4828-862b-32451eb07209","resolution":{"observed_at":"2026-08-04T10:38:13.468471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.616231Z","title":"Some inequalities satisfied by the quantities of information of Fisher and Shannon.Information and Control, 1959","venue":null,"work_id":null,"year":1959},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.616231Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:2eb3f1697e2a0bdc838fffefa0472af5592559f6425554ed1371cc8bf1d7e1a1","observation_id":"0288f043-4999-4d39-ae57-accd52fe7993","resolution":{"observed_at":"2026-08-04T10:38:13.616231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.729629Z","title":"Table meets LLM: Can large language models understand structured ta- ble data? a benchmark and empirical study","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.729629Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:77f67c884bae1c288ae38ea9ee6873329fda7c47eb1256e7424ad3e1ea6c626e","observation_id":"b155a655-7308-46ea-9ad1-262b300e244d","resolution":{"observed_at":"2026-08-04T10:38:13.729629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:13.972927Z","title":"RPT: relational pre-trained trans- former is almost all you need towards democratizing data preparation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:13.972927Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:1e9d1d72489285361ef6eb532dc90bdd715e1a41006be27ea912f794971c2474","observation_id":"7b61be02-cb01-4108-9788-3f21db2a9795","resolution":{"observed_at":"2026-08-04T10:38:13.972927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:14.044236Z","title":"Modeling tabular data using Conditional GAN","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:14.044236Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:1bb03cc81f3aba5a913acc97b7869961f9beb956c76d45c1e234ed8024021ada","observation_id":"b919253b-d97b-4def-8a74-8ac584a880c3","resolution":{"observed_at":"2026-08-04T10:38:14.044236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:14.164083Z","title":"Language-interfaced tabular oversampling via progressive imputation and self-authentication","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:14.164083Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:65a4a33e2e9ad7ef10d2f88ee6fd88ec0902bb2c6f022880edc2b19e5d6b1da7","observation_id":"e7f276ac-b902-4211-9f02-19c27665195c","resolution":{"observed_at":"2026-08-04T10:38:14.164083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T10:38:14.264744Z","title":"Generative table pre-training empowers models for tabular prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T10:38:14.264744Z"},"links":{"citing_paper":"/paper/2510.09783"},"observation_digest":"sha256:ac848d96b2e1149aba74d63393a54cf14446b8d8d7a199f09fc34fc922071023","observation_id":"d55943cf-f128-4cfc-b62e-93b35b470e95","resolution":{"observed_at":"2026-08-04T10:38:14.264744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.09783","last_updated":"2026-06-08T04:36:18Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T12:03:42.348923Z","submitted_at":"2025-10-10T18:45:29Z","title":"Large Language Models for Imbalanced Classification: Diversity makes the difference"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":33},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2510.09783."}