{"as_of":"2026-08-14T21:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1318147af53faf057d6f599664c57f20c1eb50c08d68ca490546ccc36054c455","coverage":[{"denominator":107,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:33:27.950540Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2411.10634/citation-record","integrity":"/paper/2411.10634/integrity","json":"/paper/2411.10634/citation-record.json","paper":"/paper/2411.10634"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:25.351376Z","title":"Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.351376Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:e3946715fccea9cc6389316d4b0a64c9d534098d65e27bcd08770307a2994aeb","observation_id":"ff3fdae1-ea52-4ff2-a0e8-1385a4a27ac0","resolution":{"observed_at":"2026-08-12T19:33:25.351376Z","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":"2026-08-12T19:33:25.423068Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.423068Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:7f8961d5eb62205b13729f9775088e2fc717c04e24dac875cff0e2fd22660fdd","observation_id":"594c3ca8-e596-4895-bba0-f9c6de87d881","resolution":{"observed_at":"2026-08-12T19:33:25.423068Z","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":"2026-08-12T19:33:25.469739Z","title":"Deep neural networks and tabular data: A survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.469739Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:a307e8bdd25f3f144ed6e71b364071864d15f36d71495a595d0e7cfab8810f17","observation_id":"27ce36b9-5354-4271-9c0c-b86c573c892c","resolution":{"observed_at":"2026-08-12T19:33:25.469739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01147","last_updated":"2024-06-02T14:50:49Z","snapshot_observed_at":"2026-08-13T00:17:02.151102Z","submitted_at":"2024-05-02T10:05:16Z","title":"Why Tabular Foundation Models Should Be a Research Priority","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01147","snapshot_observed_at":"2026-08-12T19:33:25.476726Z","title":"Why tabular foundation models should be a research priority","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.476726Z"},"links":{"cited_paper":"/paper/2405.01147","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:aae1f5607ebb3d43ab34010bd41b5f3ea13233f6da9d4b08de6110374bc5ec8f","observation_id":"d7e5f621-6d6d-4e4f-9dc6-59a715d21b1a","resolution":{"observed_at":"2026-08-12T19:33:25.476726Z","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":"2026-08-12T19:33:25.483155Z","title":"Empirical evaluation of performance degradation of machine learning-based predictive models–a case study in healthcare information systems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.483155Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:d3fee155d40f1a3fd537e2a6e9eed1e5a8ad7a6185492fe2824145458aea4de6","observation_id":"b9667d4b-9ab9-4494-9207-00e1308321bc","resolution":{"observed_at":"2026-08-12T19:33:25.483155Z","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":"2026-08-12T19:33:25.539664Z","title":"Temporal shifts in clinical presen- tation and underlying mechanisms of atherosclerotic disease","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.539664Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:3dbd1dda29ecbbfda37e5f353e93d00752b1966187453efe141598b87effcbaf","observation_id":"74763026-97d9-4ac6-97ab-f4868820ee3e","resolution":{"observed_at":"2026-08-12T19:33:25.539664Z","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":"2026-08-12T19:33:25.597588Z","title":"Mortality prediction of covid-19 patients at intensive care unit admission","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.597588Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ed6b75190d5eb0b0ace87488e2876aefe22971c54079c3987e68b6da6dac9dcc","observation_id":"3b6e4a25-4f90-49a3-94f6-47512bb4ec1d","resolution":{"observed_at":"2026-08-12T19:33:25.597588Z","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":"2026-08-12T19:33:25.637694Z","title":"Climate-invariant machine learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.637694Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ddfb60def7d49c35b646c9ae2b7379f470939d9d776345fa7acace2b6f6a9d7e","observation_id":"5b7683e3-10f3-4ba5-a75e-5e33da62b089","resolution":{"observed_at":"2026-08-12T19:33:25.637694Z","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":"2026-08-12T19:33:25.662670Z","title":"Dataset shift quantification for credit card fraud detection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.662670Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ce28090dffe969debec305f6844b6bcfd857e176fb2c18a1234495fd00e19cbd","observation_id":"ea2f7d73-786b-423c-875b-77c9dd4bd180","resolution":{"observed_at":"2026-08-12T19:33:25.662670Z","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":"2026-08-12T19:33:25.669273Z","title":"Hidden risks of machine learning applied to healthcare: Unintended feedback loops between models and future data causing model degradation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.669273Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:c5cb07e80ba5fcd78bc4d9911902379f0294192ee9b9225823ea253810b44683","observation_id":"f6374141-50c4-4b60-a0f8-ee6d3da9b0cf","resolution":{"observed_at":"2026-08-12T19:33:25.669273Z","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":"2026-08-12T19:33:25.675414Z","title":"Wild-time: A benchmark of in-the-wild distribution shift over time","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.675414Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:afbd6d86f7405d058bf04f77c3319da8dbc6177e865c5f389ca15976e99d3aa9","observation_id":"de5a87e8-dc49-4bec-9094-03d11d4547ae","resolution":{"observed_at":"2026-08-12T19:33:25.675414Z","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":"2026-08-12T19:33:25.681264Z","title":"Temporal domain generalization with drift-aware dynamic neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.681264Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:31ab953dae8a3d74529a4c6b4113ea9e57bd0ee0d338af4009e71658ba72ed0f","observation_id":"0285530f-251d-4d9e-975e-3cb8384f5cb8","resolution":{"observed_at":"2026-08-12T19:33:25.681264Z","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":"2026-08-12T19:33:25.799834Z","title":"Training for the future: A simple gradient interpolation loss to generalize along time","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.799834Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ad8397b963a5402a4ab24e167a953e1793adf56544de7437619daaba7bac5d4b","observation_id":"caa25ab8-a7b7-4e5e-b457-b18be6dc45fb","resolution":{"observed_at":"2026-08-12T19:33:25.799834Z","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":"2026-08-12T19:33:25.857412Z","title":"Benchmarking distribution shift in tabular data with tableshift","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.857412Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:34f4bc61a2129ac1270cab57fae9cb97ea9f93ecb47950d8363dad0f36c2287c","observation_id":"a4e2280d-2b4c-4324-87eb-439bc2b22317","resolution":{"observed_at":"2026-08-12T19:33:25.857412Z","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":"2026-08-12T19:33:25.864188Z","title":"Revisiting deep learning models for tabular data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.864188Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:d440e50147864c75b81206622de573010ecf859b14126f23511866295e7337dd","observation_id":"5d9f10ad-e02c-4833-b094-95853bbb8b3f","resolution":{"observed_at":"2026-08-12T19:33:25.864188Z","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":"2026-08-12T19:33:25.871182Z","title":"Tabular data: Deep learning is not all you need","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.871182Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:d972db7649e7edbd832a8f623c04cd4f0679463b1395db57979494f6480de28f","observation_id":"98737137-85d4-4888-8da6-70bbf49344a3","resolution":{"observed_at":"2026-08-12T19:33:25.871182Z","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":"2026-08-12T19:33:25.898054Z","title":"Why do tree-based models still outperform deep learning on typical tabular data? In Alice H","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.898054Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:3e6f540dd17c24d652da7fab6440cc42bc774cba1577ae79d4844a618e734161","observation_id":"6fdb45cd-624a-4945-9b78-fb20a8f8f60c","resolution":{"observed_at":"2026-08-12T19:33:25.898054Z","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":"2026-08-12T19:33:25.977928Z","title":"Müller, N","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:25.977928Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ed4046a3b66286f7728ca2de0ca17b1f238d6442f0ed3243fb5ed3beb6faffb4","observation_id":"f99c0e9c-234f-4e18-b5a4-43005afdb498","resolution":{"observed_at":"2026-08-12T19:33:25.977928Z","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":"2026-08-12T19:33:26.012519Z","title":"Hollmann, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.012519Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:99ebe986854688e47f313fe588a4277d050c57086569f2682ae63ce62ac3c559","observation_id":"32f11b0d-07d1-4c51-965b-a34e9fe876d4","resolution":{"observed_at":"2026-08-12T19:33:26.012519Z","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":"2026-08-12T19:33:26.029637Z","title":"Causality","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.029637Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:2755dab2d1dcc0cc0cbfb3b39b22b02ecdc1e0bbaa1ddaa6d8d39fd4aebb6a75","observation_id":"37e1cf91-1f75-4dfd-8787-89dd0004bd7b","resolution":{"observed_at":"2026-08-12T19:33:26.029637Z","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":"2026-08-12T19:33:26.040211Z","title":"Peters, D","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.040211Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:660ab810f7ca5271c7b0a7ff1a42c478f8e5048ea356938a3fd0d3419b050cb7","observation_id":"aef39098-c4c9-4baa-bcae-f74db8f87ece","resolution":{"observed_at":"2026-08-12T19:33:26.040211Z","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":"2026-08-12T19:33:26.045173Z","title":"Time2vec: Learning a vector representation of time, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.045173Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:d23abd90cecb3185977481f5267f4d7395a41708b7bae0fb25e446fa498f8af5","observation_id":"08e8536e-83fc-4242-bdec-17bef582dbcf","resolution":{"observed_at":"2026-08-12T19:33:26.045173Z","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":"2026-08-12T19:33:26.050793Z","title":"Chen and C","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.050793Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:88526f13142ef0cab2ae6c5fd56cfbc6a283b8dbd714f8839aa043323bb4cf92","observation_id":"0132240f-ee05-4ac8-8d8c-29ba4535afa0","resolution":{"observed_at":"2026-08-12T19:33:26.050793Z","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":"2026-08-12T19:33:26.055321Z","title":"Prokhorenkova, G","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.055321Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:9da99cba9d841a4051d9cdad121910c25d7ee1f56ff8e21e491d97ab2180675a","observation_id":"f9abc6af-95f3-40fe-803a-c7fb2faec365","resolution":{"observed_at":"2026-08-12T19:33:26.055321Z","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":"2026-08-12T19:33:26.061558Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.061558Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:103c2ceba073ff987eb484427d16aca3ad9fc76e9af682bc8801c01382ae42bf","observation_id":"162000c2-91cf-4b93-9431-3c9d7ed13384","resolution":{"observed_at":"2026-08-12T19:33:26.061558Z","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":"2026-08-12T19:33:26.096995Z","title":"Continuously indexed domain adaptation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.096995Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f0152ea5e4000537880e59487412f662072889a6ae17a7c7c71bc4a86b044775","observation_id":"2d8dbbb9-d32e-4163-9332-9baa57d8311f","resolution":{"observed_at":"2026-08-12T19:33:26.096995Z","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":"2026-08-12T19:33:26.135948Z","title":"Selçuk Candan, Adrienne Raglin, and Huan Liu","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.135948Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:9c89e2fb57236bcb3fb6dac47488e317e4f74a9a4722a1057084e0790b6a6d92","observation_id":"56615ff2-9eb8-416c-a886-02240d624482","resolution":{"observed_at":"2026-08-12T19:33:26.135948Z","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":"2026-08-12T19:33:26.141962Z","title":"Domain generalization via invariant feature representation","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.141962Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:20531f48978b67842caafb9c09e9637ca5368a37991526fd10bc54cb2622e42e","observation_id":"b6736780-2672-459e-816c-10d579d579eb","resolution":{"observed_at":"2026-08-12T19:33:26.141962Z","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":"2026-08-12T19:33:26.146736Z","title":"Metareg: Towards domain generalization using meta-regularization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.146736Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:6e958eb914cb8379ffa77fb91cf010d85b93fce532f452e883c9c496f6999ea2","observation_id":"6cbcf79c-1e56-491c-bce7-ecf12d6c9fc6","resolution":{"observed_at":"2026-08-12T19:33:26.146736Z","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":"2026-08-12T19:33:31.874583Z","title":"Unified deep supervised domain adaptation and generalization","venue":null,"work_id":"6a0c7d75-01ae-45e4-baab-18034a188868","year":2017},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.155215Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:fcbbf4aedad63a72df50d3da57e496e71ced59160249fe555b59d475f6d179cc","observation_id":"36d8c157-98ab-464f-bc32-2cf717fe44fd","resolution":{"observed_at":"2026-08-12T19:33:31.884729Z","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":"2026-08-12T19:33:31.788622Z","title":"Invariant risk mini- mization, 2020","venue":null,"work_id":"3b08e771-64a5-44ac-b730-5f6255cd8149","year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.167789Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:1e4b9ae16b942157c29bb4589e68e148b64416b74ddc041846d89d666a405dad","observation_id":"34797a80-248c-4abe-bf39-47cbfa082100","resolution":{"observed_at":"2026-08-12T19:33:31.842010Z","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":"2026-08-12T19:33:31.682003Z","title":"Hashimoto, and Percy Liang","venue":null,"work_id":"9fa2ec32-a9fb-402c-a32f-8b4b2fa41ecf","year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.173332Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ed31c57332603c5aa14288227f92b3c293faa8c656334e70da18691cb5fb7959","observation_id":"c96facbc-46e1-4f99-ae95-db152703f25e","resolution":{"observed_at":"2026-08-12T19:33:31.692456Z","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":"2026-08-12T19:33:31.656807Z","title":"Dauphin, and David Lopez-Paz","venue":null,"work_id":"fa7c42f5-54ba-478f-a405-ca6b1f4fb190","year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.179058Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:72e45bcae0d7b58dbf2d8e09cfb92e078a462ad6231e876f705816022373e4d8","observation_id":"a702ad57-dfd0-41fc-9dd8-7a32126a4614","resolution":{"observed_at":"2026-08-12T19:33:31.663693Z","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":"2026-08-12T19:33:31.588423Z","title":"Improving out-of-distribution robustness via selective augmentation, 2022","venue":null,"work_id":"2e86d2d8-f032-486f-a42b-5f8422fa4c08","year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.184842Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:7fa157133dfeba17c00ea117a0a1fd992fa86ffbd39b69a5bb167705c428e718","observation_id":"410de60b-bf62-44e6-ad68-1a0107f68c17","resolution":{"observed_at":"2026-08-12T19:33:31.626238Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:26.190207Z","title":"Deep coral: Correlation alignment for deep domain adaptation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.190207Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:aae8225c75739708eb1314e91f632041b0ee0f86e22ff19a6810323f8470a175","observation_id":"2f3b4bf8-b911-4cbe-985d-bef3582a24b3","resolution":{"observed_at":"2026-08-12T19:33:26.190207Z","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":"2026-08-12T19:33:31.419941Z","title":"Domain-adversarial training of neural networks","venue":null,"work_id":"3167975a-c80f-40cb-ab82-17e653ac775d","year":2016},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.229770Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f410990dbd528de01beae809b842dc07087acd05246546e500634a4930645729","observation_id":"ee1f4627-a9bd-4d80-9ac1-5fa7ab5ec8f8","resolution":{"observed_at":"2026-08-12T19:33:31.500124Z","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":"2026-08-12T19:33:31.365360Z","title":"Out-of-distribution generalization via risk extrapo- lation (rex)","venue":null,"work_id":"fae34da3-963a-4ebc-ba21-7ca6c3604aa1","year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.282497Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:8e0ed294bfe16454b0f6e4115f6dbbe8532f5d43a4348fc37c9001b2c21a80d5","observation_id":"40e6d0c6-fe6b-4b6a-8f72-ad6727c62c0e","resolution":{"observed_at":"2026-08-12T19:33:31.372664Z","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":"2026-08-12T19:33:31.344644Z","title":"Pappas, and Bernhard Schölkopf","venue":null,"work_id":"af5b20b9-e05c-410a-9111-cff7c6419621","year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.335873Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:a38786106ef422b8248e379a15664db979b159c0614b87f7a7d9c5bc99217eeb","observation_id":"a287624f-06f2-4afa-9728-e9a71c39cd71","resolution":{"observed_at":"2026-08-12T19:33:31.350990Z","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":"2107.07455","last_updated":"2022-02-11T08:23:22Z","snapshot_observed_at":"2026-08-13T18:45:08.598302Z","submitted_at":"2021-07-15T16:59:34Z","title":"Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07455","snapshot_observed_at":"2026-08-12T19:33:26.356287Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.356287Z"},"links":{"cited_paper":"/paper/2107.07455","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:6cf48395e3455dc0a6fece7670bf65aaed78a75613d3ef77c448b2055308d6e0","observation_id":"2bb3e851-8787-4e75-86e2-748250d6b86b","resolution":{"observed_at":"2026-08-12T19:33:26.356287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.15407","last_updated":"2022-09-15T09:52:12Z","snapshot_observed_at":"2026-08-13T15:13:20.357312Z","submitted_at":"2022-06-30T16:51:52Z","title":"Shifts 2.0: Extending The Dataset of Real Distributional Shifts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.15407","snapshot_observed_at":"2026-08-12T19:33:26.400515Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.400515Z"},"links":{"cited_paper":"/paper/2206.15407","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:71c8580bd9bec7846e7a83ef5cc37d0d7a79e7183de5a2322ea8573638000dd3","observation_id":"c83ff9e9-6db1-495c-8e5a-ca2e867021e8","resolution":{"observed_at":"2026-08-12T19:33:26.400515Z","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":"2026-08-12T19:33:26.407525Z","title":"On the need for a language describing distribution shifts: Illustrations on tabular datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.407525Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ccabb6f7b4fcd1e54e49c26bd6fb4f2d0eda963f5343840fd247a8f99f1d43f4","observation_id":"fde7f298-c396-4247-b972-0ffbd0e36ef6","resolution":{"observed_at":"2026-08-12T19:33:26.407525Z","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":"2026-08-12T19:33:31.308424Z","title":"Retiring adult: New datasets for fair machine learning","venue":null,"work_id":"bceb7621-7d71-4ce5-b97b-1a22a5de2f39","year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.413339Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:b45a2cc568bedddb5cee9bc7f92a179d2501bef033eda3f48ebb4c6bca8fd18d","observation_id":"76324576-359a-4181-9419-0a6dd8b32322","resolution":{"observed_at":"2026-08-12T19:33:31.314668Z","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":"2312.01792","last_updated":"2023-12-04T10:27:38Z","snapshot_observed_at":"2026-08-13T05:10:32.551732Z","submitted_at":"2023-12-04T10:27:38Z","title":"Wild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.01792","snapshot_observed_at":"2026-08-12T19:33:26.418326Z","title":"Wild-tab: A benchmark for out-of-distribution generalization in tabular regression, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.418326Z"},"links":{"cited_paper":"/paper/2312.01792","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:e21e4af7696b6217ea9f98e59b45da95d1c518ad534bc7e94bab8abc2201a2d0","observation_id":"68134b1c-2809-49a3-8903-dfeaa7e2cf2c","resolution":{"observed_at":"2026-08-12T19:33:26.418326Z","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":"2026-08-12T19:33:31.196985Z","title":"In search of lost domain generalization","venue":null,"work_id":"dfe0fded-56fa-44d3-b994-1f44821284be","year":2021},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.424407Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:67e3f6cd5de40a2fc2c635e37410093d9e2136e92d74692c55b84f4e12d19679","observation_id":"8618bc55-3502-4246-9db4-4b4375ff7467","resolution":{"observed_at":"2026-08-12T19:33:31.275374Z","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":"2026-08-12T19:33:31.151907Z","title":null,"venue":null,"work_id":"6422936a-ae3f-46a7-a22b-b397b1d65816","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.429648Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:54ff85b06bcbcbbcbbcb940a6ad77b9a18cb374c404ee58f5f2f12c655580c6e","observation_id":"de7b3495-06cf-481b-9778-0f9ff929fa10","resolution":{"observed_at":"2026-08-12T19:33:31.158582Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T19:33:31.101954Z","title":"Averaging weights leads to wider optima and better generalization","venue":null,"work_id":"8c0861f2-5397-42e7-be5b-446fd9ee1f81","year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.537386Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:83796f5fe6ee766cf833cd462f9be905c1288e468aad04401c90d2942134965c","observation_id":"9b267a98-c5f6-49d8-b674-2a07506cbd75","resolution":{"observed_at":"2026-08-12T19:33:31.136718Z","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":"2026-08-12T19:33:31.023061Z","title":"Task agnostic continual learning via meta learning","venue":null,"work_id":"e5b60a29-5dc8-4cfe-a13f-d2babd2d379a","year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.545594Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:cbcf649a836cb23512a5ef6da76c2fa5a70c982171d1cf8becc54140cc883f48","observation_id":"569a8512-e1e9-45c3-97a7-7f9dad59bc35","resolution":{"observed_at":"2026-08-12T19:33:31.052994Z","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":"2402.06971","last_updated":"2024-02-10T15:23:45Z","snapshot_observed_at":"2026-08-13T04:22:02.662473Z","submitted_at":"2024-02-10T15:23:45Z","title":"In-Context Data Distillation with TabPFN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06971","snapshot_observed_at":"2026-08-12T19:33:26.552104Z","title":"In-context data distillation with tabpfn, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.552104Z"},"links":{"cited_paper":"/paper/2402.06971","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:a94ce46d1916de2553f20e97995d600cd63c62c2581e2c1eedb80fe4fac22def","observation_id":"b9b2cccc-bd3e-4737-b01d-fb70d2511806","resolution":{"observed_at":"2026-08-12T19:33:26.552104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-12T19:33:26.563098Z","title":"Tunetables: Context optimization for scalable prior-data fitted networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.563098Z"},"links":{"cited_paper":"/paper/2402.11137","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:3615985daafab8dde2da3380e05a770f0b8d282c0a5422184d92a5c075108551","observation_id":"71fc548f-8c50-471b-a41c-9e681d0d0f6a","resolution":{"observed_at":"2026-08-12T19:33:26.563098Z","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":"2026-08-12T19:33:30.991657Z","title":"Forecastpfn: Synthetically-trained zero-shot forecasting","venue":null,"work_id":"4abc0254-ca90-4108-a812-4a6fff51b2e4","year":2023},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.624367Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:380c2f98a84b0301676d04ea744a0fa477f881ab0fb69bcacde07651b2394ca9","observation_id":"2f63f2bb-efcf-40ae-baea-6ea1ce26ebaa","resolution":{"observed_at":"2026-08-12T19:33:31.003478Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:26.684172Z","title":"Moreno-Torres, Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.684172Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:a9c1e3fa1f5e173274da94dc1692873f3ffaa798114bfaac265e3cbc9ec8e220","observation_id":"4ca4882a-b3b7-4f2a-8f79-365ad44b4e2e","resolution":{"observed_at":"2026-08-12T19:33:26.684172Z","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":"2026-08-12T19:33:30.797483Z","title":"Patterns of dataset shift","venue":null,"work_id":"a5a0f4be-d221-4ebe-ad71-f76c6b48a518","year":2014},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.692089Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f1f274e43b6cbde906312be509818b871df6a0c39b5d67fd0d816e8dcf659662","observation_id":"a40f8e2f-8f63-4820-bf29-21aa5791f5f2","resolution":{"observed_at":"2026-08-12T19:33:30.912392Z","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":"2026-08-12T19:33:30.713322Z","title":"Vanschoren, J","venue":null,"work_id":"46ca0ea9-125a-4531-a67e-4737b0479c83","year":2014},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.697741Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ab86b4cde542bdc7354286ada2b507854b53028824e7f6c06ff2270350cc806e","observation_id":"71765255-0bb3-4425-a0d2-eb692b67b360","resolution":{"observed_at":"2026-08-12T19:33:30.720844Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:26.703510Z","title":"Individual comparisons by ranking methods","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.703510Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ec25b3ece705cf50404d1c3687b72a6156c644c3f14e7846e23113daec4079bc","observation_id":"91f81c9a-26ee-43fb-927c-c51410118465","resolution":{"observed_at":"2026-08-12T19:33:26.703510Z","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":"2026-08-12T19:33:30.674295Z","title":"A simple sequentially rejective multiple test procedure","venue":null,"work_id":"04f9dfea-54d3-4d66-b07f-2e02a73ad25f","year":1979},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.714867Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:4c43baff2169b32de0de08ba3bb78b6794454862f8460201e130d88633cf7b32","observation_id":"0d358dd5-e01a-4d04-99ad-0dbc703b95c9","resolution":{"observed_at":"2026-08-12T19:33:30.680972Z","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":"2026-08-12T19:33:30.529486Z","title":"statistical comparisons of classifiers over multiple data sets","venue":null,"work_id":"954b4b02-770b-4b62-b1ac-176a81a0a660","year":2008},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.719967Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:17a9fd9712533dfe061c434f396e2ea869e34a4f48884b89163355ae26605e02","observation_id":"df7c2d50-d777-42ac-8430-dc4e5ecaf90c","resolution":{"observed_at":"2026-08-12T19:33:30.604842Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:26.724924Z","title":"Deep learning for time series classification: a review.Data Mining and Knowledge Discovery, 33(4):917–963, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.724924Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:1034198fe73c1347d364904622030395226fb3377cc0a29c1f2c6da5ddfc5644","observation_id":"3fba7415-2604-47e4-9bb2-f57077412303","resolution":{"observed_at":"2026-08-12T19:33:26.724924Z","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":"2026-08-12T19:33:26.729890Z","title":"Overcoming catastrophic forgetting in neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.729890Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f7f1d3acd5ee0b97cbe01c28b91aa072c05d7f9cfc60ce95db572456f3172788","observation_id":"9e857def-dfbf-4828-87fd-6be879f6cecb","resolution":{"observed_at":"2026-08-12T19:33:26.729890Z","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":"2026-08-12T19:33:30.456236Z","title":"Continual learning through synaptic intelligence","venue":null,"work_id":"94fd7913-15d3-4de4-a1b9-09b266cce6ab","year":2017},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.734978Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:441e80eddb13320dab8551a32b68ccad7e7045b4fc0800f06393c03abf0512a4","observation_id":"25199db8-45bb-434f-9043-0777a0ceea60","resolution":{"observed_at":"2026-08-12T19:33:30.468937Z","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":"2026-08-12T19:33:30.435631Z","title":"Efficient lifelong learning with A-GEM","venue":null,"work_id":"30ac1b7f-94d7-4490-aec6-52839f77580e","year":2019},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.811023Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:013b3d7fb5a8de008f71c543e9ee10dfabf849b28c3dd62fb2f257b6f2fb26e5","observation_id":"785a0d0d-d696-44fd-9deb-99ae4f88ec59","resolution":{"observed_at":"2026-08-12T19:33:30.442780Z","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":"2026-08-12T19:33:30.339176Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"e0f09c2b-6a53-4907-8d36-0faaa5e70eca","year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.857530Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f447a001a28b42a73bc35d22ef329e8123d315b04b3b04371d4f353e5f7f8c22","observation_id":"ae9c9b25-4441-4d14-b900-3285ff6eb350","resolution":{"observed_at":"2026-08-12T19:33:30.418341Z","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":"2026-08-12T19:33:30.316266Z","title":"Unsupervised learning of visual features by contrasting cluster assignments","venue":null,"work_id":"d6ae886a-abec-4a66-afe6-9f3d2db139cf","year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.862581Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:4e47aa98bee1159fbf02e3809effc7a6d7e49e63d968795206fbb96e1f92cd7b","observation_id":"6be977cf-6e72-4021-9fda-dcdd04c0eb7f","resolution":{"observed_at":"2026-08-12T19:33:30.324727Z","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":"2026-08-12T19:33:30.294969Z","title":"Venkateswarlu","venue":null,"work_id":"39472bd3-7a93-4955-8b63-dde9da09b88f","year":2012},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.868423Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:4a5d8653a7be8657cae0ba0a6c20be96174d9e472ac5a63dabc9d392ac31c865","observation_id":"f9e3671d-4c21-4c9e-a21d-70f9bd83c79e","resolution":{"observed_at":"2026-08-12T19:33:30.301034Z","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":"2026-08-12T19:33:30.236140Z","title":"Istanbul Stock Exchange","venue":null,"work_id":"afb70e1d-a2cb-42ab-8be6-2b492097ec17","year":2013},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.874609Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:3530982c6c50baf6aeed43161ad5957a0ea5162647ae50e1bd7ceb035344e1c2","observation_id":"c9a3ae9f-b6c3-4ac5-b0df-73a5fe59f3de","resolution":{"observed_at":"2026-08-12T19:33:30.260727Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:26.879594Z","title":"DeShazo, Chris Gennings, Juan L","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.879594Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ffbcc92aa867d11a7d43a680d6286ac9578aeccff95b3c3c5b5a051628d36255","observation_id":"2f4295b0-5f5e-4d6a-a21a-9f00c4049d03","resolution":{"observed_at":"2026-08-12T19:33:26.879594Z","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":"2026-08-12T19:33:30.137682Z","title":"Data Expo competition","venue":null,"work_id":"326958f9-f056-4395-adab-c322087e937d","year":2009},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.899690Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:bfc91f70c347c6a7298941ea2671deee8044b4883a8c6a4e374ad5bfd7bf7326","observation_id":"0c4a79ce-c30f-4982-9a5c-4022885199e0","resolution":{"observed_at":"2026-08-12T19:33:30.169530Z","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":"2026-08-12T19:33:30.075654Z","title":"Everhart, W","venue":null,"work_id":"8e5b0cc2-f617-42af-8b75-b921c62c2c85","year":1988},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:26.990045Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:3f858dc562e5324f6cc1d5e97267c27e9b4517b9db7ab9cbbed52aa8b59a8986","observation_id":"73485a16-8993-4701-a5fd-fa53c9731b55","resolution":{"observed_at":"2026-08-12T19:33:30.091550Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:27.051773Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.051773Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:8ccbd1ba01bb9223712754fb63093dec0d0a7c7642bcbb6ecb1f3b798abd7f8a","observation_id":"e0036112-5bfa-467a-bad2-89eb899360a7","resolution":{"observed_at":"2026-08-12T19:33:27.051773Z","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":"2026-08-12T19:33:30.057687Z","title":"Occupancy Detection","venue":null,"work_id":"38850748-3d0b-4d7c-93d4-42a928cdcc65","year":2016},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.057796Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:4388af87c67b1c2a83c507e2b7edf60d3a72c475af741d906b827e41c6ed3589","observation_id":"b8c19647-0600-4f20-9b8a-016e1ff7e5dc","resolution":{"observed_at":"2026-08-12T19:33:30.063238Z","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":"2026-08-12T19:33:29.924377Z","title":"Behavior of the urban traffic of the city of Sao Paulo in Brazil","venue":null,"work_id":"55759a51-35af-4380-97fc-069e10655c28","year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.063890Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f72a68a2d3523cd1502bc18d60450d86e2d2c425430419aae513a4541d39556e","observation_id":"78d84628-888c-4748-b829-6392d188ac55","resolution":{"observed_at":"2026-08-12T19:33:30.005629Z","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":"2026-08-12T19:33:29.888800Z","title":"Scikit-multiflow: A multi- output streaming framework","venue":null,"work_id":"29044b1c-d767-402c-af3a-902f736abc3a","year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.139236Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f94c370a3bde6678b16e6c2e492d8db74e54c1fbcc635a1d4772c798a7095e0e","observation_id":"d322f2f9-fa96-4ab4-85eb-5b52546af695","resolution":{"observed_at":"2026-08-12T19:33:29.894462Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:27.170828Z","title":"Use of nonclonal serum immunoglobulin free light chains to predict overall survival in the general population","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.170828Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:e4170763fd6b860a41b501c2bc155240158a2035cfafe59993b89efa9af587ed","observation_id":"2cdf0348-d978-4ad4-a666-451daa02067a","resolution":{"observed_at":"2026-08-12T19:33:27.170828Z","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":"2026-08-12T19:33:27.181462Z","title":"Prevalence of monoclonal gammopathy of undetermined significance","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.181462Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:69eacf957a841dfaa5ffc7e5c14f795a32d782ac483ab28a5b33b8465cf17a1d","observation_id":"79212911-8d47-4a1a-ad29-ac631420de4f","resolution":{"observed_at":"2026-08-12T19:33:27.181462Z","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":"2026-08-12T19:33:29.866599Z","title":"Splice-2 comparative evaluation: Electricity Pricing","venue":null,"work_id":"c45509ea-a9f9-4cfc-9769-2292c7cbc5c5","year":1999},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.186498Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:58bbb7dd0c96ea617fba20b01ffdbe195705259fbb188c962a77eb393f837e61","observation_id":"d4ceaf94-25dc-470f-992a-232a2fc77630","resolution":{"observed_at":"2026-08-12T19:33:29.873540Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:27.192986Z","title":"Learning with drift detection","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.192986Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:bb04dbb8fdf65cb0bb9cac0d89b57187dc6c5ea00423500c85e6de83a64bf788","observation_id":"71fb7712-c532-42df-b1ab-5b86613f55d7","resolution":{"observed_at":"2026-08-12T19:33:27.192986Z","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":"2026-08-12T19:33:29.845451Z","title":"Absenteeism at work","venue":null,"work_id":"e98b1835-fedb-4fd0-b864-411abc9282c4","year":2018},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.244418Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:ace63e4a8f355e570e409766d21662c5858ab29a7ad69b07196453d09fba23b1","observation_id":"f6bd39cd-c909-4e63-ab37-bd1c66cd2177","resolution":{"observed_at":"2026-08-12T19:33:29.852222Z","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":"2026-08-12T19:33:29.824019Z","title":"Heart Disease","venue":null,"work_id":"a44f38d4-a601-4679-8212-8845672aa020","year":1988},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.336692Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:69d57ba2e3e813a64b37fd38d3f6ee15e12bab8c4b6b1bcb119ba1b5e92840dd","observation_id":"d2394b2a-2327-48a9-9de0-30a2f82e6dda","resolution":{"observed_at":"2026-08-12T19:33:29.831523Z","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":"2026-08-12T19:33:29.803883Z","title":"Parking Birmingham","venue":null,"work_id":"17d1ab8e-d87d-49cb-b4ba-96762d54941b","year":2019},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.371222Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:9c8d4111605b3c5b451d13a2ff92005be309d9ef221ad8768080e4492a009409","observation_id":"1359f3fc-0b19-4ab0-8fce-3f0f4786feda","resolution":{"observed_at":"2026-08-12T19:33:29.810006Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:27.392153Z","title":"Ames, iowa: Alternative to the boston housing data as an end of semester regression project","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.392153Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:f203fffd45ea905a82994aa36190a9f13791b832ea49a988190297e40ab54307","observation_id":"9b792511-5342-4c1f-9f39-f1ea3802d6f9","resolution":{"observed_at":"2026-08-12T19:33:27.392153Z","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":"2026-08-12T19:33:29.751790Z","title":"Combining similarity in time and space for training set formation under concept drift","venue":null,"work_id":"1fc29d3b-3353-402b-87d8-0e52c6f45647","year":2011},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.396884Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:acaf701260991ae5a058668123332201c5980b62124acf9b2ae9b8f118196f06","observation_id":"47f734dd-4f80-4036-bbb1-c9b923861a0a","resolution":{"observed_at":"2026-08-12T19:33:29.777064Z","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":"2305.16704","last_updated":"2023-05-26T07:47:21Z","snapshot_observed_at":"2026-08-13T11:32:43.032297Z","submitted_at":"2023-05-26T07:47:21Z","title":"A Closer Look at In-Context Learning under Distribution Shifts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16704","snapshot_observed_at":"2026-08-12T19:33:27.456551Z","title":"A closer look at in-context learning under distribution shifts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.456551Z"},"links":{"cited_paper":"/paper/2305.16704","citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:5be64d14a632a9d074927c06d6a0dbfb6e6f927cfd20254a231fbee7258a50bc","observation_id":"a0bb2b86-9129-4396-b1ef-cbfe339751aa","resolution":{"observed_at":"2026-08-12T19:33:27.456551Z","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":"2026-08-12T19:33:29.573173Z","title":"In healthcare, this can ensure diagnostic and prognostic models remain reliable as data shifts over time","venue":null,"work_id":"4b2aad92-1352-4925-a175-a91900561ee4","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.515736Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:06a8489d5664c42ac7959222685da4b7d9da2632155890f695b51b6ea8fae9bc","observation_id":"f83d5219-a0f9-494d-b357-3814def7f3d4","resolution":{"observed_at":"2026-08-12T19:33:29.688331Z","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":"2026-08-12T19:33:29.547677Z","title":"Our Bayesian approach for tackling distribution shift provides a new perspective that can spur further methodological innovations","venue":null,"work_id":"b46628db-9d0b-442e-9b8a-3392493ad115","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.545779Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:9f7c65e0d9c7e0688a004f85d8dfabb9b6efbbc3515e3594ad4c5cce27ac1679","observation_id":"9b8ba75d-4fa7-4a57-8120-a067c88cf691","resolution":{"observed_at":"2026-08-12T19:33:29.554985Z","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":"2026-08-12T19:33:29.519945Z","title":null,"venue":null,"work_id":"df4b609b-7e9a-4400-9fa1-5f0f8c98a65a","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.587493Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:e25b9b65edc88b11d41f6fde0ceb8bb6367de71c76a014e4f03da03bb255d21d","observation_id":"aa6c1a6e-cf7e-4425-990e-d32afe18348a","resolution":{"observed_at":"2026-08-12T19:33:29.527602Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T19:33:29.496970Z","title":"However, we note that these costs are one-time, while the resulting model can be applied with minimal energy usage","venue":null,"work_id":"27099952-96ef-43bd-8999-5e2fc8c35f0d","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.594094Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:52803df37a2e9134d911bdb2cb7beed667fd0ab37a3a41a1ac526fc72953126c","observation_id":"5aa0ef8b-beb3-4cba-9758-5a59240ae8f8","resolution":{"observed_at":"2026-08-12T19:33:29.504162Z","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":"2026-08-12T19:33:29.473524Z","title":null,"venue":null,"work_id":"b889defb-18bb-433f-bfa8-3ac8fc5721b9","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.599831Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:c91907a09584ee47d3614d5a9dbfa0691ef61f583ce6613e5b6e1776570261a2","observation_id":"864b2f7f-8f88-41bc-8de7-c205558ba0a9","resolution":{"observed_at":"2026-08-12T19:33:29.481256Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T19:33:29.360365Z","title":null,"venue":null,"work_id":"1e259c9b-2465-47f0-841b-fcdbca3afbc7","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.605696Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:31be8371b385d88c0225b6ee1d90d1a62d2f5b078e09bc2ea0b36f3aac8e2484","observation_id":"105c4e6f-2799-4f98-a4b7-b21487ea37ac","resolution":{"observed_at":"2026-08-12T19:33:29.429833Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T19:33:29.185202Z","title":null,"venue":null,"work_id":"422e82d3-24f6-45df-b55a-83833198f812","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.676243Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:b70a133de36097bbc7998271d6491edfb0a80c43dec7cddcb7d585ed113e7a19","observation_id":"7371968e-a09f-4ea3-ac8a-4d8aa50c969b","resolution":{"observed_at":"2026-08-12T19:33:29.265035Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T19:33:29.163622Z","title":"Both TabPFN-base and Drift-Resilient TabPFN underwent preprocessing optimization that utilized 8 CPUs, 1 GPU, and 62.5 GB RAM across 300 runs, each lasting between 0.5 to 1 hour","venue":null,"work_id":"a417c754-7466-4de4-a2ef-61a36c6c27e2","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.723564Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:92806a07bb9648e7f5fa62a3169b76eeccbadd10b68e813028c6cab82e2309d7","observation_id":"279e387b-6b11-4eb2-8783-cb23bb4a6d6d","resolution":{"observed_at":"2026-08-12T19:33:29.170751Z","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":"2026-08-12T19:33:29.144024Z","title":"Slowness in traffic (%)","venue":null,"work_id":"c9316733-9771-4059-9d60-2a5248052b85","year":2009},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.805078Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:2f0fc13afb6471df9b48a0d8068709cdaea8d55f809d8a9aa8d958ff1b610ee8","observation_id":"78f3127a-2f8e-4086-9a86-fd957411ab57","resolution":{"observed_at":"2026-08-12T19:33:29.148832Z","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":"2026-08-12T19:33:29.126413Z","title":"Comprehensive evaluations across 18 synthetic and real-world datasets","venue":null,"work_id":"99345de2-6d92-404d-933d-8eccaadfb7d5","year":2024},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.900848Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:81bab4864831648dfc5280c3a63bbe261ab371117d7883bee0390002d98ac747","observation_id":"900c2ac6-e4ed-48e7-9676-07dd049905e8","resolution":{"observed_at":"2026-08-12T19:33:29.131984Z","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":"2026-08-12T19:33:29.109329Z","title":"Conclusions and Limitations","venue":null,"work_id":"3151fb7e-c770-4dc0-b412-b28d10f226d0","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.906339Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:068802f601326a215cd567af2428048f38ed2a42025e5e650001f838016395c5","observation_id":"1d996789-4471-4a8d-b05d-2d3ebc5e6640","resolution":{"observed_at":"2026-08-12T19:33:29.114842Z","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":"2026-08-12T19:33:29.085667Z","title":null,"venue":null,"work_id":"27c510d1-773b-411f-a750-9bda93c17e9c","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.911110Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:11fcbeae7b2a89db414797a651d9eff0015a634d5d16303ba002c5b0b2fa0bd6","observation_id":"30954458-05e8-4e91-9432-cc717ea9bd7c","resolution":{"observed_at":"2026-08-12T19:33:29.095538Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T19:33:28.970646Z","title":"Reproducibility","venue":null,"work_id":"67710c63-5564-4fe8-a0e6-11d274a7b4c5","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.917074Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:4e60ec2d2f77f0340a61454d773cf1b9a0ac5d128e8612c88794677b879d95d5","observation_id":"1eabdb3d-75b6-4d42-a9ed-efa986d0cfc4","resolution":{"observed_at":"2026-08-12T19:33:29.069849Z","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":"2026-08-12T19:33:28.917281Z","title":"Reproducibility","venue":null,"work_id":"3e75b726-e55c-448f-8382-d8766bffee48","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.922348Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:0954c9e40535d119e65a8a31f5e6972c5c5ac72198a19acd10f4a92e6c4ef788","observation_id":"57ca138a-a260-4241-a294-6ea39632a71b","resolution":{"observed_at":"2026-08-12T19:33:28.923479Z","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":"2026-08-12T19:33:28.898543Z","title":"Reproducibility","venue":null,"work_id":"feb3d29d-ea7d-4c55-92ef-78d6d823e8d3","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.927727Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:be912b3f4da104a0013bbd250762cb0fff6e2311bbad3bca99f0a1b8d1bbca6e","observation_id":"ac32025f-c888-4457-8fa7-b3371bf84703","resolution":{"observed_at":"2026-08-12T19:33:28.903439Z","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":"2026-08-12T19:33:28.876852Z","title":"We report 95% Confidence Intervals in all our quantitative results and mark this appropriately in the paper","venue":null,"work_id":"288fb103-5d23-4bfc-95ab-9d950863c18a","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.933447Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:7216ed35160bfe9860de4c2709cc73caee13e3520aaf51397916e3f6ca94a420","observation_id":"9a46ee45-3113-4560-a637-944838b1d9d5","resolution":{"observed_at":"2026-08-12T19:33:28.883505Z","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":"2026-08-12T19:33:28.850746Z","title":"See Section A.3 for details","venue":null,"work_id":"650487bb-4279-45c5-980f-e2501f81a96e","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.939492Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:71be6cf873b1c43e3f28ae8593e2ad41b55dfd34457cb754e76b095acfa1d203","observation_id":"625c03f4-d3ba-402d-8d86-59589763991a","resolution":{"observed_at":"2026-08-12T19:33:28.860031Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:33:27.945426Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.945426Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:290e955842f19e38016aee67b485fee522a2e7ae4587f676a8b4a4696be8a5bc","observation_id":"e8885fb7-df4a-49c7-b235-7a380178b1c6","resolution":{"observed_at":"2026-08-12T19:33:27.945426Z","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":"2026-08-12T19:33:28.812782Z","title":"Please see Section A.2 for details","venue":null,"work_id":"491b2d09-6783-4adb-8058-9802d4302610","year":null},"citing_paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:27.950540Z"},"links":{"citing_paper":"/paper/2411.10634"},"observation_digest":"sha256:e164e6a0ce7dea5341f84aecf722196dfb57b81c4e0701406246085515d932a1","observation_id":"cb54159e-dcd9-4179-9623-80e16b10c2ae","resolution":{"observed_at":"2026-08-12T19:33:28.820935Z","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"}}],"paper":{"arxiv_id":"2411.10634","last_updated":"2024-11-15T23:49:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T21:01:45.574185Z","submitted_at":"2024-11-15T23:49:23Z","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":54,"verified_exact":0,"verified_fuzzy":46},"total_outbound_references":107},"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 100 of 107 outbound references and 0 inbound Pith citation observations for arXiv:2411.10634."}