{"as_of":"2026-08-07T18:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:946b2bac02d83415378d20f0b0a76ee17616813122dca5e5dfbfc9f1b0a4992b","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T15:17:44.471904Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.01890/citation-record","integrity":"/paper/2606.01890/integrity","json":"/paper/2606.01890/citation-record.json","paper":"/paper/2606.01890"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":"1607.06450","doi":"10.1007/978-3-319-32025-0","metadata_source":"pith","pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Layer Normalization","venue":"stat.ML","work_id":"20a2d720-0046-4c7c-bcd6-327ec8143f69","year":2016},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:665fcd9785b7b0c744462772d2aa7d5c21ef292d9a358a7b66dc19a99b1a8358","observation_id":"b838bd6a-dde2-436c-b2bd-b0729044906c","resolution":{"observed_at":"2026-07-01T22:36:17.093070Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:17:44.471904Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:12db0c0b857b806247dc9d1866265468d3153a468284fe28235af24bcf9ee111","observation_id":"88d4f4ff-9a4a-4b0e-b773-cebe855b1624","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"BERT: Pre-training of deep bidirectional transformers for lan- guage understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:942fc99547f62b9b99b9481fc421013dcbbdf97b76dfb034c5fc2d8feee8b7be","observation_id":"5e47179e-ee8d-4608-8ed5-fb8fe35bc074","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17944","last_updated":"2024-06-21T19:59:54Z","snapshot_observed_at":"2026-07-06T17:36:32.068270Z","submitted_at":"2024-02-27T23:59:01Z","title":"Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey","version":4},"cited_work":{"arxiv_id":"2402.17944","doi":"10.48550/arxiv.2402.17944","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17944","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Large language models (llms) on tabular data: Prediction, generation, and understanding–a survey","venue":"arXiv (Cornell University)","work_id":"f76c00f3-2715-4a68-bad0-80db7ba41f0f","year":2024},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"cited_paper":"/paper/2402.17944","citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:90a7d2ce395ba8598e9b44effac577f185098915be19d34dce82d61306968af4","observation_id":"cc42d362-d9ce-49c8-be16-43b6a9a4f479","resolution":{"observed_at":"2026-07-01T22:36:17.086569Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.06678","last_updated":"2020-12-11T23:31:23Z","snapshot_observed_at":"2026-07-06T10:23:43.246202Z","submitted_at":"2020-12-11T23:31:23Z","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","version":1},"cited_work":{"arxiv_id":"2012.06678","doi":"10.48550/arxiv.2012.06","metadata_source":"pith","pith_arxiv_id":"2012.06678","snapshot_observed_at":"2026-07-10T17:07:25.686746Z","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","venue":"cs.LG","work_id":"22746e9d-0ec8-4c75-8591-5655c9ec57e1","year":2020},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"cited_paper":"/paper/2012.06678","citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:60f856eee2aecb03f511f6a27419e2b033d4633a43bde3276ba582d003d13966","observation_id":"cfd87a04-b32a-4b41-b330-da324b1de3df","resolution":{"observed_at":"2026-07-01T22:36:17.089826Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-24T02:53:07.527821+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-24T02:53:07.527821+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:17:44.471904Z","title":"Tabbie: Pretrained representations of tabular data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:35ce96b73b783f8d5d6eeb29d0689d4b56d402cf59ac9579904864892bab4353","observation_id":"1074242d-1eb0-4f70-81f0-24ae21c346f6","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"and Yoon, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:72d5a5a3d052fcf64d247ba8436a4dee1aed9439f6474a76adc3780bcf3db48f","observation_id":"9c059b4e-f44c-405a-8771-77613c30c777","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"1807.03748","doi":"10.1609/aaai.v36i10.21390","metadata_source":"pith","pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Representation Learning with Contrastive Predictive Coding","venue":"cs.LG","work_id":"7b08a1d4-d565-424e-9c86-6ef244b7b90a","year":2018},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:61f2329231de4f79f202e4ba1eba277faf8846de79b0f914190a865526daaeb5","observation_id":"4e1b8284-c008-457c-a7d4-5680e165459b","resolution":{"observed_at":"2026-07-01T22:36:17.083385Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:17:44.471904Z","title":"The web data commons schema.org table corpora","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:41aea97b33edf6c5d8e5ad34edc714f09d15fc050a04221cad4ab667b4f849c7","observation_id":"6a153d52-6be8-4eb9-9c09-3af2c8a4eb94","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:7bed9145d4dc56ef476d8c599dd98ee87470fc965df517b7eaba0470d09c7cad","observation_id":"109b2906-3ed1-40ed-a468-0abc9c307fec","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"Towards cross-table masked pretraining for web data mining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:a9ee8f8c6b5bf979caa843c09b657aae79c5a71659eb796cbdbeaf5fffb76ddd","observation_id":"44eea660-8adc-42f5-bedb-0e1941bac8bd","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"Mixed- type tabular data synthesis with score-based diffusion in latent space","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:c459cbad351ac60000ebfb1a2d9bf6108b6444f0c6e44056281f4f67bfade6c3","observation_id":"93fbed12-541e-45d3-b5d4-7c892a8be440","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:9cf6405be58e0427ff4a58fe260b98e841aa8299383dedd30fdd84c72d3ae05f","observation_id":"db423b0c-888d-41df-888a-e9ab9ea3280d","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"These methods are effective at modeling feature interactions, handling heterogeneous feature types, and achieving strong predictive performance under task-specific supervision","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:55f28864b22c46ff767f6d571473d94f7aa2a7e395cb5734cf1b2aa68c87b2c6","observation_id":"8c0247c7-f815-4df6-98a9-c00cab3fbd62","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","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":"2026-06-28T15:17:44.471904Z","title":"These approaches typically assume fixed and well-defined feature spaces within a table or benchmark dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T15:17:44.471904Z"},"links":{"citing_paper":"/paper/2606.01890"},"observation_digest":"sha256:26a96bbcd38c2f76c64acef1551dbdebe6edd51c72884ffd942a8f0f56a0a688","observation_id":"2b618405-c258-426a-8b0a-73cbb2dfec3d","resolution":{"observed_at":"2026-06-28T15:17:44.471904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.01890","last_updated":"2026-06-01T08:34:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T00:45:34.253772Z","submitted_at":"2026-06-01T08:34:25Z","title":"Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":11,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":15},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2606.01890."}