{"as_of":"2026-08-13T14:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dc9553a88776197eaa2791b6a6af97347ae5d4f1369bfca35abbc99bedb46352","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:39:25.454631Z","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":"2026-07-01T22:06:16.757809Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-08-07T15:39:25.454631Z","title":"Tabred: Analyzing pit- falls and filling the gaps in tabular deep learning benchmarks.arXiv preprint arXiv:2406.19380, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14312","last_updated":"2025-05-20T13:00:43Z","snapshot_observed_at":"2026-08-10T03:56:07.717164Z","submitted_at":"2025-05-20T13:00:43Z","title":"MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:39:25.454631Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2505.14312"},"observation_digest":"sha256:a22cea331a22304ad9657a76ada3278e24d249ddeb29a2c5c754e3e632847991","observation_id":"a80e8505-8013-46b2-b9fb-fbb429d484c8","resolution":{"observed_at":"2026-08-07T15:39:25.454631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":"2406.19380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-07-01T22:06:16.757809Z","title":"arXiv preprint arXiv:2406.19380 , year=","venue":null,"work_id":"adfe9b5e-73e1-4fd1-b7d9-78ed87ba4a8f","year":2024},"citing_paper":{"arxiv_id":"2506.16791","last_updated":"2025-11-03T18:47:03Z","snapshot_observed_at":"2026-07-06T21:45:06.566500Z","submitted_at":"2025-06-20T07:14:48Z","title":"TabArena: A Living Benchmark for Machine Learning on Tabular Data","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-19T08:41:35.789878Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2506.16791"},"observation_digest":"sha256:a3edc0009c9914e334963c3950a5621795bacafdb3b6ffd21d73bdeda645c225","observation_id":"0d204686-1935-424b-8f0a-84c159799f42","resolution":{"observed_at":"2026-05-19T08:42:12.603055Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-08-06T15:02:20.433829Z","title":"Rubachev, N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17020","last_updated":"2025-07-22T21:21:37Z","snapshot_observed_at":"2026-08-10T13:30:52.982576Z","submitted_at":"2025-07-22T21:21:37Z","title":"Ethics through the Facets of Artificial Intelligence","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T15:02:20.433829Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2507.17020"},"observation_digest":"sha256:d8cc50a4d2cd16730ba032b593258753bb5b447df79762c4503abd56b297884c","observation_id":"e364db2c-766b-467a-bf16-e84ccfc4460b","resolution":{"observed_at":"2026-08-06T15:02:20.433829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":"2406.19380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-07-01T22:06:16.757809Z","title":"arXiv preprint arXiv:2406.19380 , year=","venue":null,"work_id":"adfe9b5e-73e1-4fd1-b7d9-78ed87ba4a8f","year":2024},"citing_paper":{"arxiv_id":"2605.06290","last_updated":"2026-05-07T13:56:49Z","snapshot_observed_at":"2026-08-11T14:39:30.823456Z","submitted_at":"2026-05-07T13:56:49Z","title":"Data Language Models: A New Foundation Model Class for Tabular Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T10:06:34.627253Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2605.06290"},"observation_digest":"sha256:b3656d1fdaf8fcf503b5abc215e86b76c7730d9cd0e63b003a393ca1ecb46300","observation_id":"3c8a42d4-76fa-4d4d-83e6-8d77f2469e84","resolution":{"observed_at":"2026-05-11T20:11:10.255788Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":"2406.19380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-07-01T22:06:16.757809Z","title":"arXiv preprint arXiv:2406.19380 , year=","venue":null,"work_id":"adfe9b5e-73e1-4fd1-b7d9-78ed87ba4a8f","year":2024},"citing_paper":{"arxiv_id":"2605.22644","last_updated":"2026-05-21T15:50:40Z","snapshot_observed_at":"2026-08-12T12:23:14.587330Z","submitted_at":"2026-05-21T15:50:40Z","title":"Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-05-22T08:06:52.309619Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2605.22644"},"observation_digest":"sha256:cdd618e0bab34a9d00e4867435045822da311ecff03ade8dca42b7572e5ef423","observation_id":"03a6b07f-6804-4008-b6e5-291c01a54db9","resolution":{"observed_at":"2026-05-22T08:11:17.511561Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":"2406.19380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-07-01T22:06:16.757809Z","title":"arXiv preprint arXiv:2406.19380 , year=","venue":null,"work_id":"adfe9b5e-73e1-4fd1-b7d9-78ed87ba4a8f","year":2024},"citing_paper":{"arxiv_id":"2606.02384","last_updated":"2026-06-01T15:33:43Z","snapshot_observed_at":"2026-07-06T23:42:49.173711Z","submitted_at":"2026-06-01T15:33:43Z","title":"TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T15:43:46.621365Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2606.02384"},"observation_digest":"sha256:736dc03d836913fd6bd691e5ca199c8d53498ab710e870fcb5d9096325718a0b","observation_id":"02b382f7-d581-4429-b37e-6b7e3a082fb4","resolution":{"observed_at":"2026-07-01T22:06:16.759834Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks","version":4},"cited_work":{"arxiv_id":"2406.19380","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19380","snapshot_observed_at":"2026-07-01T22:06:16.757809Z","title":"arXiv preprint arXiv:2406.19380 , year=","venue":null,"work_id":"adfe9b5e-73e1-4fd1-b7d9-78ed87ba4a8f","year":2024},"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":15,"source":"pdf_text","source_observed_at":"2026-06-30T06:59:14.626274Z"},"links":{"cited_paper":"/paper/2406.19380","citing_paper":"/paper/2606.30410"},"observation_digest":"sha256:78f0c68f19e1ede800db700bce81d6bc89ba466213cb7f1a77547f314796ccba","observation_id":"ae0a23ea-f214-44e5-9b51-7df635035e10","resolution":{"observed_at":"2026-06-30T07:04:21.409584Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.19380/citation-record","integrity":"/paper/2406.19380/integrity","json":"/paper/2406.19380/citation-record.json","paper":"/paper/2406.19380"},"outbound":[],"paper":{"arxiv_id":"2406.19380","last_updated":"2024-10-24T17:54:37Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T23:33:24.436027Z","submitted_at":"2024-06-27T17:55:31Z","title":"TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.19380."}