{"as_of":"2026-08-22T13:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5dc60571b4b577eea75fc8698e2fca2e96ced65168e8e1cdc0cb6d0910d94025","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T11:38:54.961085Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2606.03681/citation-record","integrity":"/paper/2606.03681/integrity","json":"/paper/2606.03681/citation-record.json","paper":"/paper/2606.03681"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T11:38:54.961085Z","title":"2026 , eprint=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:c21f448df615448e18b944c537f615521308e3732aa5bd4754ea14bf563e88a0","observation_id":"1fd53947-9020-4222-a480-b8abcf75fa2d","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Deep Neural Networks and Tabular Data: A Survey , journal =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:a60371a28eee7e437f913a00b295a4a65fecb83fcdc8bdb18e39205f2df2a327","observation_id":"19930821-18f2-4eae-8766-04af63db3f86","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Proceedings of the 41st International Conference on Machine Learning , series =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:b396f8e9c1819ff79a8e5884d78c3e0299b215d0cf45e4f046f9c38a8eb369e2","observation_id":"fe624afe-af01-42eb-b8a2-e248e9e76782","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"The Eleventh International Conference on Learning Representations (ICLR) , publisher =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:dfe4ecd296c287f6630274ec7b5d637608eeb1c02b159a001e143f8de5762fad","observation_id":"86e20bde-6cdb-4193-ba0b-6a838ca95b3a","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Accurate predictions on small data with a tabular foundation model , journal =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:5cde897f8ad4439cb3705d30925d62c5eb753bf2f976a35c2e41f4d40aced80f","observation_id":"040dd822-72df-45a9-b4d5-776bf64f219f","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Proceedings of the 42nd International Conference on Machine Learning , series =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:f57a9842d69c3ba297d5ed076bb4ff8e83da44b1cbe420c1570de5e12a784ee6","observation_id":"c3aa1669-ffce-41d2-841b-73f2e9125fc7","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2602.11139","doi":"10.48550/arxiv.2602.11139","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"TabICLv2: A better, faster, scalable, and open tabular foundation model.arXiv preprint arXiv:2602.11139","venue":"Open MIND","work_id":"402348e3-a3ef-4455-a95a-4b7f04970bc2","year":2026},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:628bcfd89a5639c4b7134c6a058e5e0fe189b7966b0d3875b9d513652fd56dbc","observation_id":"d8514775-207a-4392-8f9e-a796847252f0","resolution":{"observed_at":"2026-07-02T01:36:25.980969Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-06-28T11:38:54.961085Z","title":"2025 , eprint =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:bbf0cc7341e52bd4a640d19dbca8a14276ce5842a210bbbc37f604938e9b0af8","observation_id":"7c2a6c03-af0c-4abd-9b90-9466f8110827","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"2025 , eprint =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:8250b787afc8e52976d40fa2be85d50a5e0722793f8f355234f6c5fe2313f5da","observation_id":"d9228856-1299-4369-b1c9-8c118bb53244","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:17bac01fe47d0be75c06841905d3e403c61bd6a583fff3fcefb0c21bd3c00d8c","observation_id":"8cb1c8ee-56b9-4171-96a6-17aac68e009e","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"2024 , url =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:85e0773b6b162f79b1dc5ab36a4d3eaf7563b2a934e14ff3e98a0bc4b64f3956","observation_id":"1899922f-bf09-4b52-97db-02fa6007f0b6","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"2026 , url =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:1b8bfa35829659b8c237d0b37b3ad61a4b435d7a6f4ac87b38bae414a63ed31e","observation_id":"eeceaeae-ac58-4280-a049-2b6079dcf9e0","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"2023 , url =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:7315da06d2304eb4af58bd4d2b0cc3da16e4ed45adafbd53013810b75ba782ef","observation_id":"8b9a4e88-9527-43cf-9bb0-6e032ce73943","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"modded-nanogpt: Speedrunning the","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:d625baaa4ddbf27330d9c61844f383b4f2a2bdb2c67130304980e14f0b3f5408","observation_id":"dd6c5abf-d265-4c2f-b852-7d26a525e5cd","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Proceedings of the 40th International Conference on Machine Learning , series =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:95eb5e20d6d3480b02a2dfaf7f2dd1e787d95c1b05a9446c28b4136b836680f7","observation_id":"da177a48-052a-4775-bf16-e6692fdfbe29","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"and Golestan, Keyvan and Yu, Guangwei and Caterini, Anthony L","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:96b889b5acf3de03c58a86ba62c9ecb4fd1ee41219b66923739dfb2890fabde2","observation_id":"c7fdd945-0375-46df-9e70-0dbe5216527d","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Advances in Neural Information Processing Systems (NeurIPS) , volume =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:96ff84f3bbacf798999fc1f288c95bacdf85aa72540273a7a8a467cd0980dcc6","observation_id":"ee688d5e-52c6-4519-92af-dd0231fc0943","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"2022 , eprint =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:8a8bff4aeb55916270e4916232b840d6702d3e70f2d82e947084261413b81625","observation_id":"c18ec3ab-fe62-46a6-8a48-892ff531aa50","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"First Conference on Language Modeling (COLM) , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:c6213c6d9f1ec8afbe130ef2d4fe67ccbb1bfcf9264b3386799e528b67572330","observation_id":"436cb731-ebd1-4293-ab03-884648c76717","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":", title =","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:7f5e70554967a24e19c2496da8757498b48d7f44b998807fe8ba857fd1652874","observation_id":"bd0b5fbd-1d28-4003-a389-7090ab19a703","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Advances in Neural Information Processing Systems (NeurIPS) , volume =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:e7076b81513e4e9d29c0e0977ce15689602e6bdeb3b33324a40277fe5f23ef6e","observation_id":"a8fbc1ca-79cd-476e-9b7a-167b96bd3bd9","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","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":"2026-06-28T11:38:54.961085Z","title":"Advances in Neural Information Processing Systems (NeurIPS) , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-28T11:38:54.961085Z"},"links":{"citing_paper":"/paper/2606.03681"},"observation_digest":"sha256:52465c7575cd4eb6305466376db9ded01ae6ccf82e2097e28209f42430fec8e5","observation_id":"aa0a945c-bfe8-411e-b2dd-4cfce56ec4ca","resolution":{"observed_at":"2026-06-28T11:38:54.961085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.03681","last_updated":"2026-06-02T14:04:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T05:28:52.845850Z","submitted_at":"2026-06-02T14:04:31Z","title":"Speedrunning Tabular Foundation Model Pretraining"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":22},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2606.03681."}