{"as_of":"2026-08-10T00:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:013a05f541d1ec9f67f638133ae60567be6ab30ac7db10170d26d2043ef91392","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T14:45:18.813055Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T06:59:14.626274Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-06-30T07:04:21.534397Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"cited_work":{"arxiv_id":"2512.19602","doi":null,"metadata_source":"pith","pith_arxiv_id":"2512.19602","snapshot_observed_at":"2026-06-30T07:04:21.534397Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","venue":"cs.CV","work_id":"5fd466c7-51b9-4caa-a98c-e7d6f29150ce","year":2025},"citing_paper":{"arxiv_id":"2606.30410","last_updated":"2026-06-29T14:55:52Z","snapshot_observed_at":"2026-08-03T09:53:02.738738Z","submitted_at":"2026-06-29T14:55:52Z","title":"Beyond IID: How General Are Tabular Foundation Models, Really?","version":1},"reference_index":148,"source":"pdf_text","source_observed_at":"2026-06-30T06:59:14.626274Z"},"links":{"cited_paper":"/paper/2512.19602","citing_paper":"/paper/2606.30410"},"observation_digest":"sha256:a708255b867065b2dd442154ffb44186264b9bf8b1bd94052fa5f8f83cacc43c","observation_id":"3deef451-4f67-42fb-a367-3584f6454cb1","resolution":{"observed_at":"2026-06-30T07:04:21.535881Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2512.19602/citation-record","integrity":"/paper/2512.19602/integrity","json":"/paper/2512.19602/citation-record.json","paper":"/paper/2512.19602"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T14:45:18.709535Z","title":"Foundation models defining a new era in vision: a survey and outlook","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.709535Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:912dcf8c42d3e54a70736d59998b903b1239707427b7e90c2734821aac637a55","observation_id":"bfab448b-4a72-4af3-943f-91e61195da73","resolution":{"observed_at":"2026-08-03T14:45:18.709535Z","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":"2026-08-03T14:45:18.713592Z","title":"Automated car- diovascular magnetic resonance image analysis with fully convolutional networks.Journal of cardiovascular magnetic resonance, 20(1):65, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.713592Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:f9397341df7d4f420421ae1ebafb79bc7494ac0dabb138278c49f513dcab23fc","observation_id":"8674c7a4-9378-4638-be0d-a13594718c06","resolution":{"observed_at":"2026-08-03T14:45:18.713592Z","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":"2026-08-03T14:45:18.717059Z","title":"Predicting stroke outcome: a case for multimodal deep learn- ing methods with tabular and ct perfusion data.Artificial Intelligence in Medicine, 147:102719, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.717059Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:0a90f7a3896dc14c3a34a133fa610b6fa15158cb8615b5878b4fc2b5d6895e7c","observation_id":"900ac5ee-81ce-48fe-8dca-7f0f1294b6a3","resolution":{"observed_at":"2026-08-03T14:45:18.717059Z","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":"2026-08-03T14:45:18.720229Z","title":"Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge.IEEE Transactions on Medical Imaging, 40(12):3543–3554, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.720229Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:387d7c54a06e533958f3e0d1364a5f5c63c3b41fb26a5c1b36a602d9dbdecec0","observation_id":"2d7937a3-3b39-4a59-bd40-1244972ddb4d","resolution":{"observed_at":"2026-08-03T14:45:18.720229Z","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":"2026-08-03T14:45:18.723725Z","title":"Xgboost: A scalable tree boosting system","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.723725Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:de2fa7eee9185ea5b98a4dfcc17b3349888d165f38105fed6297ae61589560e6","observation_id":"99009de1-ee74-4650-b26a-fe11eac77420","resolution":{"observed_at":"2026-08-03T14:45:18.723725Z","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":"2026-08-03T14:45:18.726910Z","title":"Tip: Tabular-image pre- training for multimodal classification with incomplete data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.726910Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:dcfd292e6f30f0872ab3f428b108e72160aa7650cc228ef8680be157e947ad99","observation_id":"68419ab1-40c2-48a9-b673-d5e46c7d12ca","resolution":{"observed_at":"2026-08-03T14:45:18.726910Z","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":"2026-08-03T14:45:18.730092Z","title":"Stil: Semi-supervised tabular-image learning for comprehensive task-relevant information exploration in multimodal classifi- cation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.730092Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:9dd48e59f87c43d50efc0bcf1f1b0272b531a6dc2205390ffa7cb8ee59997b86","observation_id":"7f70cb0a-89ea-40e1-bbcd-8d6aff52b018","resolution":{"observed_at":"2026-08-03T14:45:18.730092Z","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":"2026-08-03T14:45:18.732845Z","title":"Prediction of pathological complete re- sponse to neoadjuvant chemotherapy in breast cancer using deep learning with integrative imaging, molecular and demo- graphic data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.732845Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:b979ebabf3199a621042decee8b22984b9a21d99f0946dff40b4fd3b3ad22cd3","observation_id":"777af6eb-f1e6-4d40-a53a-6d512028945a","resolution":{"observed_at":"2026-08-03T14:45:18.732845Z","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":"2026-08-03T14:45:18.738743Z","title":"Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.Medical Image Analysis, 102: 103503, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.738743Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:d84bef5a55a65b37f1750cae85b14f731165838e0490edffbe53b935dfa9d49b","observation_id":"cb8ddad3-8766-4aeb-9ec3-52781c73ed5b","resolution":{"observed_at":"2026-08-03T14:45:18.738743Z","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":"2026-08-03T14:45:18.741562Z","title":"Time and the patient–physician relationship.Journal of gen- eral internal medicine, 14(Suppl 1):S34, 1999","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.741562Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:a09af35bd1200e2852bd45775718dbab57dd1986e4d23eda52659266a2e71760","observation_id":"2b0c508f-cb91-4331-87cf-915280a17d67","resolution":{"observed_at":"2026-08-03T14:45:18.741562Z","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":"2026-08-03T14:45:18.744409Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.744409Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:79875e379532b2f9078b075ef562dafd78f3ea8c1f8bc4e8ea860d4fea4c0f24","observation_id":"6e2c9752-1a1c-4f24-99e0-3d1db3655c65","resolution":{"observed_at":"2026-08-03T14:45:18.744409Z","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":"2026-08-03T14:45:18.747643Z","title":"Why do tree-based models still outperform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.747643Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:b23f44c0275a994e70c75706fe28ce7d3200b0816f9e6046d3159c04189f5d91","observation_id":"f458b991-46d7-4c00-a5eb-cd71314148eb","resolution":{"observed_at":"2026-08-03T14:45:18.747643Z","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":"2026-08-03T14:45:18.750513Z","title":"Best of both worlds: Multimodal contrastive learning with tabular and imaging data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.750513Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:50d35d27f849d4419bc3fc7b7b8ca0cce1908806b448180143da8f9472bc1a81","observation_id":"74308899-9b20-4665-8444-e05967d5cf52","resolution":{"observed_at":"2026-08-03T14:45:18.750513Z","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":"2026-08-03T14:45:18.753503Z","title":"Can spatiotemporal 3d cnns retrace the history of 2d cnns and im- agenet? InProceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.753503Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:0a933d1addb5c980fd743b012db37030f0793980215839313e13b94f5027a7f0","observation_id":"2dd8fc48-3a4f-4401-837c-ba0654dae379","resolution":{"observed_at":"2026-08-03T14:45:18.753503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14998","last_updated":"2026-05-08T09:54:13Z","snapshot_observed_at":"2026-07-06T20:55:13.966734Z","submitted_at":"2025-03-19T08:49:55Z","title":"Tables Guide Vision: Learning to See the Heart through Tabular Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14998","snapshot_observed_at":"2026-08-03T14:45:18.756176Z","title":"Tables guide vision: Learning to see the heart through tabular data.arXiv preprint arXiv:2503.14998, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.756176Z"},"links":{"cited_paper":"/paper/2503.14998","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:811cb8197e9911144b9c42765427609e75bb1b21392ee346a85dc4039fda72c5","observation_id":"97dc18f6-626b-4085-8c2b-d254a6777704","resolution":{"observed_at":"2026-08-03T14:45:18.756176Z","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":"2026-08-03T14:45:18.759560Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.759560Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:f47133bb03535670031e13a03bc6222c63cb38adf0d702992c6f213ac83d8dd6","observation_id":"e6cb2bc8-e203-419f-9623-9a69fa157909","resolution":{"observed_at":"2026-08-03T14:45:18.759560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01848","last_updated":"2023-09-16T09:33:32Z","snapshot_observed_at":"2026-07-06T13:27:49.894090Z","submitted_at":"2022-07-05T07:17:43Z","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01848","snapshot_observed_at":"2026-08-03T14:45:18.762662Z","title":"Tabpfn: A transformer that solves small tabular classification problems in a second.arXiv preprint arXiv:2207.01848, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.762662Z"},"links":{"cited_paper":"/paper/2207.01848","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:a7c6a87a0f65a30b156d2878324db3083e6a53453d6cbdac2d141bb8e827d1e4","observation_id":"a66ae2de-5de0-4f68-ab39-2ea72eb82c59","resolution":{"observed_at":"2026-08-03T14:45:18.762662Z","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":"2026-08-03T14:45:18.765635Z","title":"Dvm-car: A large-scale automotive dataset for visual marketing research and applications, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.765635Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:c235975103544d820cf4b1715967a34a9849189bb6e776f5eec8cdbdd54725a1","observation_id":"8ba0a0af-365d-4c49-a688-7e87d74cfb22","resolution":{"observed_at":"2026-08-03T14:45:18.765635Z","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":"2026-08-03T14:45:18.768350Z","title":"Tabular insights, visual impacts: transferring expertise from tables to images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.768350Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:06d1bfa2d82bd02e9a9979e6fa2df86d5cd015930cacffd051a48adb797a84be","observation_id":"49ebc3e9-9c6d-4724-9a2d-91af5863b71d","resolution":{"observed_at":"2026-08-03T14:45:18.768350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-08T04:19:16.139935Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-08-03T14:45:18.771139Z","title":"Table foundation models: on knowledge pre-training for tabular learning.arXiv preprint arXiv:2505.14415, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.771139Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:71b1bbe9a1ceb1533603f76a6ace47162e9780876c7fa9967338722b764110db","observation_id":"2314dd8a-fbf9-4352-a56f-abd27e69907b","resolution":{"observed_at":"2026-08-03T14:45:18.771139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T14:45:18.774258Z","title":"Matryoshka representation learning.Advances in Neural Information Processing Systems, 35:30233–30249, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.774258Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:ce67a40915f9d670036f0d51d1941aee902736b562d0539c2830c4ed37bb035e","observation_id":"e1490aec-121f-4a5c-8dc9-a4736c365b0e","resolution":{"observed_at":"2026-08-03T14:45:18.774258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.19264","last_updated":"2025-08-06T17:01:13Z","snapshot_observed_at":"2026-08-06T14:24:18.376376Z","submitted_at":"2025-07-25T13:39:34Z","title":"SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.19264","snapshot_observed_at":"2026-08-03T14:45:18.776911Z","title":"Simmlm: A simple framework for multi-modal learning with missing modality","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.776911Z"},"links":{"cited_paper":"/paper/2507.19264","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:369478954edc43b618fe8858ccd267303d220791232edc194226c26a083abba5","observation_id":"2a952cae-d0e4-4d65-93a8-57ddbb0b3396","resolution":{"observed_at":"2026-08-03T14:45:18.776911Z","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":"2026-08-03T14:45:18.779736Z","title":"Classification and regres- sion by randomforest.R news, 2(3):18–22, 2002","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.779736Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:73e7b547d6ed52a7fa921016754d08147f4ff6c4091122caa3c5c9bc29261a36","observation_id":"292ba139-b839-4822-8af4-98738103b52c","resolution":{"observed_at":"2026-08-03T14:45:18.779736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05564","last_updated":"2025-05-24T08:05:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-08T13:25:04Z","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05564","snapshot_observed_at":"2026-08-03T14:45:18.782771Z","title":"Tabicl: A tabular foundation model for in-context learning on large data.arXiv preprint arXiv:2502.05564, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.782771Z"},"links":{"cited_paper":"/paper/2502.05564","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:d41be8b85c62044e28194f6edf937c908e837556ede036b4412371d1c3c4173d","observation_id":"e24e2c49-210b-4108-b1db-a63b5925b7a0","resolution":{"observed_at":"2026-08-03T14:45:18.782771Z","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":"2026-08-03T14:45:18.785810Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.785810Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:ba25117307f11cf9a8cc1c5738ff4dacb96a283615aa01b170f0f2138b1e35a4","observation_id":"d80995c8-5cbb-449b-9a5f-5fcb2d62c7f5","resolution":{"observed_at":"2026-08-03T14:45:18.785810Z","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":"2026-08-03T14:45:18.788605Z","title":"A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to alzheimer’s disease.Neuroimage, 189:276–287, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.788605Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:62d5e78495a070e38ec0b3a92bf3b9da8e6fc52272819d1dfa4a47360aea53bd","observation_id":"5e829653-60c3-4532-9aca-3bc9f7767cdc","resolution":{"observed_at":"2026-08-03T14:45:18.788605Z","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":"2026-08-03T14:45:18.792024Z","title":"Uk biobank: an open access resource for identifying the causes of a wide range of com- plex diseases of middle and old age.PLoS medicine, 12(3): e1001779, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.792024Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:017ee38c457e071073b709c1e1414481924a7cccb7d3525d66bee3708c03f91a","observation_id":"be8b3850-39c1-4a0f-a3d3-5bb46a3500d4","resolution":{"observed_at":"2026-08-03T14:45:18.792024Z","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":"2026-08-03T14:45:18.794928Z","title":"Long-term cancer survival prediction using multimodal deep learning.Scientific Reports, 11(1):13505, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.794928Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:40edfdb36c1dc57af0f3f03c5c6661f422285eae201bf41bdd084cd6b0e41bd9","observation_id":"0f5b7f00-73a6-492a-a448-698da5d24086","resolution":{"observed_at":"2026-08-03T14:45:18.794928Z","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":"2026-08-03T14:45:18.797817Z","title":"Attention is all you need.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.797817Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:2bfdc1bd932b6d0f4fbc7bc1f9eb3536b9ccdb026ed6f4596a3b35c3b662497b","observation_id":"15121c18-2f0a-4b75-9d4a-7bcba6f937ca","resolution":{"observed_at":"2026-08-03T14:45:18.797817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.10213","last_updated":"2025-07-14T12:31:28Z","snapshot_observed_at":"2026-08-06T21:44:56.226984Z","submitted_at":"2025-07-14T12:31:28Z","title":"Boosting Multimodal Learning via Disentangled Gradient Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.10213","snapshot_observed_at":"2026-08-03T14:45:18.800781Z","title":"Boosting multimodal learning via disentangled gradient learning.arXiv preprint arXiv:2507.10213, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.800781Z"},"links":{"cited_paper":"/paper/2507.10213","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:27f8b08d734a9b2a3d36a26d350be157ef026bed243dd6263e53da95de42c064","observation_id":"3a935e74-6a33-4dad-b7ff-9d17661416e3","resolution":{"observed_at":"2026-08-03T14:45:18.800781Z","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":"2026-08-03T14:45:18.803887Z","title":"Daft: A universal module to interweave tabular data and 3d images in cnns.NeuroImage, 260:119505, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.803887Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:62570177b1c89e22aa4e21c10fbc4eaf7aa788bfa4dec5e65852d5f9b79363da","observation_id":"c7ad50e1-67db-4009-bce0-ab0f769e8f10","resolution":{"observed_at":"2026-08-03T14:45:18.803887Z","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":"2026-08-03T14:45:18.806761Z","title":"A closer look at deep learning methods on tabular datasets.arXiv preprint arXiv:2407.00956, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.806761Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:3b8fb07ff5ba14858d9b374586e315987831b8419362835adccad2feda85d798","observation_id":"f5d512a0-1d0d-4ba6-b670-1350f3d768d9","resolution":{"observed_at":"2026-08-03T14:45:18.806761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-06T23:27:24.356320Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-03T14:45:18.809812Z","title":"A survey of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.809812Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:114e4616f20286a0dd8745e5c1c8daa899054b71c12ecaa002bb5831c276110a","observation_id":"b25cc000-d803-44ca-bf7a-c6212556fd2f","resolution":{"observed_at":"2026-08-03T14:45:18.809812Z","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":"2026-08-03T14:45:18.813055Z","title":"Multi-transsp: Multimodal transformer for survival prediction of nasopharyngeal carcinoma patients","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.813055Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:8d4e7b4921697295b485abe05c9f89227560ebbc57c511a790be6206b522333c","observation_id":"67a671e5-4563-4e0a-ae08-5693fa023bf7","resolution":{"observed_at":"2026-08-03T14:45:18.813055Z","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":"2026-08-03T14:45:18.735776Z","title":"2, 5, 6, 8, 1","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values","version":2},"reference_index":252,"source":"pdf_text","source_observed_at":"2026-08-03T14:45:18.735776Z"},"links":{"citing_paper":"/paper/2512.19602"},"observation_digest":"sha256:ba7e62c7f6e07c7121f7252a905ea9ff07c7c60c69e22e71f9437d8c1a4b1674","observation_id":"42783bed-2442-4247-9b15-0b30803e604f","resolution":{"observed_at":"2026-08-03T14:45:18.735776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.19602","last_updated":"2026-05-26T10:01:26Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T00:39:24.527466Z","submitted_at":"2025-12-22T17:35:32Z","title":"No Data? No Problem: Robust Vision-Tabular Learning with Missing Values"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":35},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2512.19602."}