{"as_of":"2026-08-14T12:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:79eee8a7c2d7e0ccfcffdeda0c2fc7dfd23044ae05c849b2d7185a0943a9afbf","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:38:39.465674Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:20:02.370073Z","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":"2026-06-30T07:04:21.323657Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-08-06T23:20:02.370073Z","title":"J., Lefebvre, F., Brison, G., Perez-Lebel, A., and Varoquaux, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18421","last_updated":"2025-07-14T06:09:12Z","snapshot_observed_at":"2026-08-13T21:22:47.097035Z","submitted_at":"2025-06-23T09:02:04Z","title":"TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:02.370073Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2506.18421"},"observation_digest":"sha256:9007f7636f2ce155170ced76657195c3bce7b60f90bb58b6fc9149303e8c8512","observation_id":"48d2a58e-7701-44fd-a6bd-4891431660d6","resolution":{"observed_at":"2026-08-06T23:20:02.370073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-08-06T19:19:59.589569Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.05904","last_updated":"2025-07-08T11:45:29Z","snapshot_observed_at":"2026-08-10T18:04:14.914978Z","submitted_at":"2025-07-08T11:45:29Z","title":"Universal Embeddings of Tabular Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:19:59.589569Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2507.05904"},"observation_digest":"sha256:0bd4a9873f07aa9d861b311337957f6520e735ad1d2b7a1e633dfdeff2c85773","observation_id":"d38ef5f4-dcb3-4971-8352-f661fb003b97","resolution":{"observed_at":"2026-08-06T19:19:59.589569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-08-03T14:45:18.771139Z","title":"Table foundation models: on knowledge pre-training for tabular learning.arXiv preprint arXiv:2505.14415, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-12T21:14:07.494476Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.771139Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:1b6712e6a26e4fcced633b08cd6424e17bec76e395c597e7837c31884b3e6b62","observation_id":"2314dd8a-fbf9-4352-a56f-abd27e69907b","resolution":{"observed_at":"2026-08-03T14:45:18.771139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":"2505.14415","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-06-30T07:04:21.323657Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning, May 2025","venue":null,"work_id":"152c5344-586a-4af0-b87e-cd486c09dac5","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-10T21:55:05.622635Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:fe14c22df322ba0e5620df1bfd6d8f4a7c123abf42a2475694e2131fcf6388a0","observation_id":"1577594e-4499-4b24-80e0-646c96ccf321","resolution":{"observed_at":"2026-05-11T16:21:07.886717Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":"2505.14415","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-06-30T07:04:21.323657Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning, May 2025","venue":null,"work_id":"152c5344-586a-4af0-b87e-cd486c09dac5","year":2025},"citing_paper":{"arxiv_id":"2605.10616","last_updated":"2026-05-11T14:12:05Z","snapshot_observed_at":"2026-08-14T08:45:08.094982Z","submitted_at":"2026-05-11T14:12:05Z","title":"MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-12T04:53:40.505699Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2605.10616"},"observation_digest":"sha256:985527db69a0ba550e13f117d4590ca86570812f4c7daa3e885adbb948b785f8","observation_id":"9efba0da-0413-42e0-9d99-05436f1b14b7","resolution":{"observed_at":"2026-05-12T05:51:25.033687Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":"2505.14415","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-06-30T07:04:21.323657Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning, May 2025","venue":null,"work_id":"152c5344-586a-4af0-b87e-cd486c09dac5","year":2025},"citing_paper":{"arxiv_id":"2606.30410","last_updated":"2026-06-29T14:55:52Z","snapshot_observed_at":"2026-08-03T09:53:02.738738Z","submitted_at":"2026-06-29T14:55:52Z","title":"Beyond IID: How General Are Tabular Foundation Models, Really?","version":1},"reference_index":119,"source":"pdf_text","source_observed_at":"2026-06-30T06:59:14.626274Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2606.30410"},"observation_digest":"sha256:3dccdaecda5606f766a47c0ac200e18ebac60b1947aa3e97436e2e627f3adeab","observation_id":"46208e56-f6a2-42b8-8eb5-68e868d71d9f","resolution":{"observed_at":"2026-06-30T07:04:21.325344Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.14415/citation-record","integrity":"/paper/2505.14415/integrity","json":"/paper/2505.14415/citation-record.json","paper":"/paper/2505.14415"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T15:38:36.320105Z","title":"Achiam, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:36.320105Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:55aadba3c661ef163c2abdda6a335468c9908813435b4f5f7eb86244686088a1","observation_id":"05daca70-f481-4555-a2b3-c152619b463d","resolution":{"observed_at":"2026-08-07T15:38:36.320105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-13T15:58:13.809876Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T15:38:37.045005Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.045005Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:f166237c9a973a91e67226876993f9121b206b77429fcc07d7bd40ab01baf67e","observation_id":"c2f4bf66-8f57-4a9d-96bc-e0fb19492045","resolution":{"observed_at":"2026-08-07T15:38:37.045005Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:41.380399Z","title":"Koshil, T","venue":null,"work_id":"0a75b220-4f70-448d-83f8-4db3c053be4d","year":2024},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.428498Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:7320361d01fe208f08fcf24503ab200f872dc62974f8274575c468183c481348","observation_id":"a5b8e220-0245-46ad-a9ca-7be97b992e7a","resolution":{"observed_at":"2026-08-07T15:38:41.473724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02527","last_updated":"2025-02-04T17:49:44Z","snapshot_observed_at":"2026-08-13T14:12:39.149822Z","submitted_at":"2025-02-04T17:49:44Z","title":"TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02527","snapshot_observed_at":"2026-08-07T15:38:37.539366Z","title":"Liu and H.-J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.539366Z"},"links":{"cited_paper":"/paper/2502.02527","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:084e697589614b7dd2f0320cee7826d34857f0b72eab5ce74a20fb5a5c2cfd49","observation_id":"5754cf65-c4a3-43bf-9834-6b0b9260a335","resolution":{"observed_at":"2026-08-07T15:38:37.539366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.09405","last_updated":"2017-12-26T21:00:04Z","snapshot_observed_at":"2026-08-12T04:50:42.826181Z","submitted_at":"2017-12-26T21:00:04Z","title":"Advances in Pre-Training Distributed Word Representations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.09405","snapshot_observed_at":"2026-08-07T15:38:37.704376Z","title":"Mikolov, E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.704376Z"},"links":{"cited_paper":"/paper/1712.09405","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:581a05852e3204400fd8229c9d490f0f246a4a578f308f96511828423151f4f3","observation_id":"3c996fa5-e22e-471f-8a49-d66da0a24bb8","resolution":{"observed_at":"2026-08-07T15:38:37.704376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-07T15:38:37.815110Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.815110Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:125264f599c78258ff44538700f7cdc330748c9b5ef604ab295754e56841c9d9","observation_id":"6b782cd1-602f-4e26-963e-d1598e0adf66","resolution":{"observed_at":"2026-08-07T15:38:37.815110Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:41.177158Z","title":"Reimers and I","venue":null,"work_id":"963ba807-2e96-4eec-af10-c03dbf857bd0","year":2019},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.112959Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:9eff73b82cde4695dadde6ab4723f56e0ee38130c55f1ca499a2ddedb9ef3224","observation_id":"369f8a73-20c8-45f4-b690-4e58c41bb318","resolution":{"observed_at":"2026-08-07T15:38:41.260103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10084","last_updated":"2019-08-27T08:50:17Z","snapshot_observed_at":"2026-08-14T05:02:11.716316Z","submitted_at":"2019-08-27T08:50:17Z","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10084","snapshot_observed_at":"2026-08-07T15:38:38.231330Z","title":null,"venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.231330Z"},"links":{"cited_paper":"/paper/1908.10084","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:c67d2abe3c8a23e16ba0d646c10282985486a0cebcd73252ad390908ca2d433c","observation_id":"7e4c48b1-9577-4006-aeaf-3d6183262aeb","resolution":{"observed_at":"2026-08-07T15:38:38.231330Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:41.001894Z","title":"14 KNOWLEDGE PRE -TRAINING OF TABLE FOUNDATION MODELS - SEPTEMBER 9, 2025 R","venue":null,"work_id":"55eab5bb-fcae-45aa-be7d-cc1c81e9b834","year":2025},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.399445Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:9d9389bec417be1447479c1d249d74bc4a46740e61ab8c06dd295ff6e8c26ff2","observation_id":"0c2f275b-f1f8-4076-b79a-5488b3166a53","resolution":{"observed_at":"2026-08-07T15:38:41.088949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:40.831868Z","title":"Spinaci, M","venue":null,"work_id":"e2b687fd-a421-4fc7-b252-5ca6dab9ea04","year":2024},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.493562Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:54ed4b6b0ceb4735e09d05609af9f176bc44e37fd5e483dcd38b3835e804a8c2","observation_id":"6838a05f-8b83-45f0-abb7-8bc683c618a6","resolution":{"observed_at":"2026-08-07T15:38:40.915967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:40.669686Z","title":"Thomas, J","venue":null,"work_id":"2da99cc4-6a0a-4fc8-8e2e-d065e81d9d2e","year":2024},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.582683Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:232f800733dec813315f2f91729e9eead8fdbe1cf2423c91f98d887c9820e5a9","observation_id":"812f3ba6-6b66-4e7a-a2d5-f280d620f0ce","resolution":{"observed_at":"2026-08-07T15:38:40.748265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14160","last_updated":"2025-03-01T12:02:55Z","snapshot_observed_at":"2026-08-12T22:40:26.667421Z","submitted_at":"2024-09-21T14:43:54Z","title":"Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14160","snapshot_observed_at":"2026-08-07T15:38:38.702636Z","title":"Varoquaux, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.702636Z"},"links":{"cited_paper":"/paper/2409.14160","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:8a2f3b78d0ef320bacbc6e209ea6babe65fce27838022ce7e297aa9731fc07e4","observation_id":"0a300e75-9d49-4ec3-86e2-26831997de0b","resolution":{"observed_at":"2026-08-07T15:38:38.702636Z","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":"2026-08-07T15:38:38.960868Z","title":"Ye, S.-Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.960868Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:39dbe005ba929038ad3eed57c22c8653d0ca2f9142bdcbdb5908ad3ccd9e106f","observation_id":"c2a7d7e9-d8f6-491a-8a91-d5aca0cf8617","resolution":{"observed_at":"2026-08-07T15:38:38.960868Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:40.501526Z","title":"Zhang, X","venue":null,"work_id":"f94f449b-ad0d-4288-bfd6-5cf1d755d0a3","year":2024},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:39.133957Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:4f6d3800e0173cfd69b930d64da5c0e696879ea14d2fa1f9fd65e4e91205eef1","observation_id":"859769ec-bfd1-4494-b384-107db2296c7d","resolution":{"observed_at":"2026-08-07T15:38:40.590633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:40.301481Z","title":"The column information is crucial to supplement context for the transformers (Kim et al., 2024)","venue":null,"work_id":"ef6306a6-be42-4c20-96f4-c2a8b67270c5","year":2025},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:39.245358Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:1b7d41b8c28d99a5104b357923d7ba610a26b96d3af96b4a84b0ddfda4ee985a","observation_id":"3fda673a-fc41-4e98-90b9-59d708ede783","resolution":{"observed_at":"2026-08-07T15:38:40.388359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:39.922358Z","title":"For the runtime, it measures the total time for data preparation, hyperparameter optimization, and prediction","venue":null,"work_id":"3b46b0cd-83fd-4d06-9b82-d84b0828c5a2","year":2011},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:39.396332Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:f13ec4838eae8941a3a4a1419872a444662378b6bd1c58185dfb4e4d5893df96","observation_id":"5fb3ed96-fdce-4bcb-bee5-73d17ef89985","resolution":{"observed_at":"2026-08-07T15:38:40.011182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:39.770384Z","title":"For each baseline, some additional details were considered","venue":null,"work_id":"30682c60-454a-4685-838a-24356693e018","year":2024},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":256,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:39.465674Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:e3d61ec548d5ae76b3372bc5b04e93fc44c4d5c880ad877a289a4806aeee84e2","observation_id":"79cd26cb-4cb7-4262-926a-c1160e17d0ac","resolution":{"observed_at":"2026-08-07T15:38:39.839027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:40.128981Z","title":"For models without native handling missing values, we imputed with the mean for numerical features, and treated as another category for categorical features","venue":null,"work_id":"ed644d81-5890-458e-9e7f-e8bdfdb6b7e0","year":2022},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":500,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:39.330898Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:e118162d511aa1f363c089767aeef13d14ab84e661d8b8af00f0307b20ffe410","observation_id":"ae3cbade-d8cd-4294-9b7b-82073b76ec50","resolution":{"observed_at":"2026-08-07T15:38:40.210026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03266","last_updated":"2024-01-16T20:15:18Z","snapshot_observed_at":"2026-08-13T20:37:30.031207Z","submitted_at":"2023-10-05T02:37:09Z","title":"UniPredict: Large Language Models are Universal Tabular Classifiers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03266","snapshot_observed_at":"2026-08-07T15:38:38.827416Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:38.827416Z"},"links":{"cited_paper":"/paper/2310.03266","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:5b550262f65b8f2e4c42ca8cd95a97ff8cae6286b08d70dd025842d3f27f0b8b","observation_id":"40638564-2700-44a8-a020-dc504a4023e0","resolution":{"observed_at":"2026-08-07T15:38:38.827416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05564","last_updated":"2025-05-24T08:05:47Z","snapshot_observed_at":"2026-08-13T01:47:13.052232Z","submitted_at":"2025-02-08T13:25:04Z","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05564","snapshot_observed_at":"2026-08-07T15:38:37.990674Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.990674Z"},"links":{"cited_paper":"/paper/2502.05564","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:fc9f73428235ac2e07c59c3448c36b4a459ff4b18308076108a823a595f73ce7","observation_id":"ccf32c95-9c0e-4e22-bbbf-093bb681edee","resolution":{"observed_at":"2026-08-07T15:38:37.990674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-07T15:38:36.578911Z","title":"Bommasani, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:36.578911Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:70a755e2bd71f8269c407730bd0e27715f4cadfa804075c9ece4afbb8e472ffa","observation_id":"04dfa0f8-5e58-47cc-b96c-65236f251363","resolution":{"observed_at":"2026-08-07T15:38:36.578911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00281","last_updated":"2024-06-01T03:24:31Z","snapshot_observed_at":"2026-08-14T08:23:15.482542Z","submitted_at":"2024-06-01T03:24:31Z","title":"Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00281","snapshot_observed_at":"2026-08-07T15:38:36.735423Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:36.735423Z"},"links":{"cited_paper":"/paper/2406.00281","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:5e6770ea0418ed2106c19192b31291c3870d60df1edde2cf012b67af23ddf311","observation_id":"abacee11-92d7-4654-8e4a-4426bc68c310","resolution":{"observed_at":"2026-08-07T15:38:36.735423Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:41.828821Z","title":"12 KNOWLEDGE PRE -TRAINING OF TABLE FOUNDATION MODELS - SEPTEMBER 9, 2025 L","venue":null,"work_id":"d663b8b5-11ec-480d-8a3a-caa46785c627","year":2025},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:36.406314Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:a2e99fe454ece4e91e62d21d83c2583a67a0215c7d6b0078b0a70075f730b1ec","observation_id":"7382e971-adee-4228-a66a-fc1a7a415593","resolution":{"observed_at":"2026-08-07T15:38:41.983251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11137","last_updated":"2024-10-21T16:48:06Z","snapshot_observed_at":"2026-08-14T10:46:40.249634Z","submitted_at":"2024-02-17T00:02:23Z","title":"TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11137","snapshot_observed_at":"2026-08-07T15:38:36.978304Z","title":"Feuer, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:36.978304Z"},"links":{"cited_paper":"/paper/2402.11137","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:f68bcae3f5a62e0a22ed2ea8f7500760e45aa0cbdb6df1c41606be8ccd9b24ed","observation_id":"7cc20e83-7ade-40a9-ae58-01cf800dbc2d","resolution":{"observed_at":"2026-08-07T15:38:36.978304Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:38:41.594812Z","title":"Herzig, T","venue":null,"work_id":"8ef91b0c-b0a0-4099-bc61-bc05d11920cf","year":2021},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.172244Z"},"links":{"citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:ae7e8047c44048e26c72d22a1b7a44cfeadd9e2b5a054c7e9ae71b7c1a74734b","observation_id":"b1b7c798-9836-4d1c-8fb0-e8765b32c604","resolution":{"observed_at":"2026-08-07T15:38:41.679330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13396","last_updated":"2025-01-23T06:15:47Z","snapshot_observed_at":"2026-08-13T16:26:26.752136Z","submitted_at":"2024-05-22T07:13:55Z","title":"Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13396","snapshot_observed_at":"2026-08-07T15:38:36.898481Z","title":"den Breejen, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:36.898481Z"},"links":{"cited_paper":"/paper/2405.13396","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:7e62d01ad62020baee801750e567e3dc52dec63f1d8441680e26c285d36254e2","observation_id":"6b40c6c7-0223-421a-9c73-3418f3ae3131","resolution":{"observed_at":"2026-08-07T15:38:36.898481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04491","last_updated":"2025-01-15T16:02:08Z","snapshot_observed_at":"2026-08-12T23:28:11.383109Z","submitted_at":"2024-07-05T13:29:30Z","title":"Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04491","snapshot_observed_at":"2026-08-07T15:38:37.319197Z","title":"Holzm¨uller, L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T15:38:37.319197Z"},"links":{"cited_paper":"/paper/2407.04491","citing_paper":"/paper/2505.14415"},"observation_digest":"sha256:cd4fa4b5e0a1c1d4de082f5de19417dad7b6c38702fc09b155b9e6b690ceed71","observation_id":"3f910b30-ae96-41ed-923f-204d60c466ca","resolution":{"observed_at":"2026-08-07T15:38:37.319197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T08:20:15.424888Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":27},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 6 inbound Pith citation observations for arXiv:2505.14415."}