{"as_of":"2026-08-07T10:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a0bb3f35141f401bf17bc912972ae29d8352abd8b905897afb1158f83b89a64","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T06:16:48.755086Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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":"2026-08-01T03:19:39.259982Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.21906","snapshot_observed_at":"2026-08-01T03:19:39.259982Z","title":"Baciak, and Xiaofeng Yang","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25164","last_updated":"2026-07-28T00:25:05Z","snapshot_observed_at":"2026-08-06T21:24:36.278441Z","submitted_at":"2026-07-28T00:25:05Z","title":"OrganLens: Organ-Specific Representation Learning for CT Foundation Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T03:19:39.259982Z"},"links":{"cited_paper":"/paper/2605.21906","citing_paper":"/paper/2607.25164"},"observation_digest":"sha256:77c1c9622b37a6b75e48d089334fff0b74d3620957956d22c05414d9736293ba","observation_id":"cd9d31d4-004a-40ec-afd5-72ebd2e5cb6d","resolution":{"observed_at":"2026-08-01T03:19:39.259982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.21906/citation-record","integrity":"/paper/2605.21906/integrity","json":"/paper/2605.21906/citation-record.json","paper":"/paper/2605.21906"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.02701","last_updated":"2022-11-04T18:35:00Z","snapshot_observed_at":"2026-07-06T14:14:38.788226Z","submitted_at":"2022-11-04T18:35:00Z","title":"MONAI: An open-source framework for deep learning in healthcare","version":1},"cited_work":{"arxiv_id":"2211.02701","doi":"10.48550/arxiv.2211.02701","metadata_source":"pith","pith_arxiv_id":"2211.02701","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MONAI: An open-source framework for deep learning in healthcare","venue":"cs.LG","work_id":"5bb8ee7d-31fa-4bb6-aef2-8b87a6d465b4","year":2022},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2211.02701","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:1f9adb6e10b8253240beacc87f05f7f75c40b73b36d9054a9319844e6892c7c7","observation_id":"033edfe1-9024-4519-9e06-09474e3f084c","resolution":{"observed_at":"2026-05-25T06:20:24.487441Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.17209","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T16:29:57.783621Z","title":"Scaling self-supervised and cross-modal pretraining for volumetric ct transformers","venue":null,"work_id":"5c6f4a01-0d65-4a1a-9289-abde90eecb2e","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:bcc9b6ab43a225a5583d919c71e6203d873da7f52191a9f2b14995895815a0f4","observation_id":"4dfee9cf-5833-4a2b-9623-88d57fdaf4bf","resolution":{"observed_at":"2026-05-25T06:20:24.493675Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.06830","last_updated":"2025-09-08T16:04:12Z","snapshot_observed_at":"2026-08-04T23:07:19.210528Z","submitted_at":"2025-09-08T16:04:12Z","title":"Curia: A Multi-Modal Foundation Model for Radiology","version":1},"cited_work":{"arxiv_id":"2509.06830","doi":"10.48550/arxiv.2509.06830","metadata_source":"arxiv_reference","pith_arxiv_id":"2509.06830","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dancette, J","venue":"ArXiv.org","work_id":"e5b486a1-e44a-4919-b11f-c54125df0dab","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2509.06830","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:20a60873d6419a78c588370a1dc860c400f20c7670c164ebe99bed0b99ae7d98","observation_id":"932361e9-a8d9-4429-8a05-cb3f0d8fe772","resolution":{"observed_at":"2026-05-25T06:20:24.475861Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16588","last_updated":"2024-04-12T09:38:33Z","snapshot_observed_at":"2026-08-07T09:40:32.614733Z","submitted_at":"2023-09-28T16:45:46Z","title":"Vision Transformers Need Registers","version":2},"cited_work":{"arxiv_id":"2309.16588","doi":"10.48550/arxiv.2309.16588","metadata_source":"pith","pith_arxiv_id":"2309.16588","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Vision Transformers Need Registers","venue":"cs.CV","work_id":"57106da4-5420-4778-94eb-e821589aa7a0","year":2023},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2309.16588","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:6273d673970110afe4e38e79a74f7e4431f510298a9cb57fcaaa57d44130827f","observation_id":"b79fdc5d-7ec8-4473-81c9-caf2d384de8d","resolution":{"observed_at":"2026-05-25T06:20:24.464451Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:146814eb677762d88343d9972ee90ee48891cb58433b6acf9a131bd80b4f726b","observation_id":"7ee30f1f-6b40-4915-b9c4-fcd7c5c27d05","resolution":{"observed_at":"2026-05-25T06:20:24.507043Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"semanticscholar.org/CorpusID:208547601","venue":null,"work_id":"244384e3-8008-4c24-ab41-9e3b1c84f577","year":2023},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:8039816917ec5d2a75de1be427178135e267e1abbc761fa7fd545c8f5bfaab41","observation_id":"568eb556-f38d-4c48-9fb9-1ccf9fba7dbe","resolution":{"observed_at":"2026-05-25T10:16:53.585806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.7840134","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"89d730f9-62bf-469a-b3bb-8662367f5ea4","year":2023},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:a118ae11d8cfa8f2291d879664a276afa602853018f95371bae50c645cf2bdaf","observation_id":"e72b48a1-34e0-41f6-bed7-5785a1db89c4","resolution":{"observed_at":"2026-05-25T06:20:24.161296Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-22T16:22:39.921712+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T16:22:39.921712+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12534","last_updated":"2024-08-22T16:38:45Z","snapshot_observed_at":"2026-07-06T19:04:43.716629Z","submitted_at":"2024-08-22T16:38:45Z","title":"Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge","version":1},"cited_work":{"arxiv_id":"2408.12534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.12534","snapshot_observed_at":"2026-07-04T16:49:58.521392Z","title":"Automatic organ and pan-cancer segmentation in abdomen ct: the flare 2023 challenge.arXiv preprint arXiv:2408.12534","venue":null,"work_id":"fe9ff6e5-e63c-44b2-bed4-1565d7ec0288","year":2023},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2408.12534","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:608f3ea10eb95527adf2e60c42bf8863b9603e5b4b30419a254e16da877b5ec5","observation_id":"f9f74932-a0cd-4bc0-8a0f-06add8cf6a3b","resolution":{"observed_at":"2026-05-25T06:20:24.453418Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16512","last_updated":"2025-03-07T19:38:42Z","snapshot_observed_at":"2026-08-03T16:54:44.297715Z","submitted_at":"2024-10-21T21:05:04Z","title":"TIPS: Text-Image Pretraining with Spatial awareness","version":2},"cited_work":{"arxiv_id":"2410.16512","doi":"10.48550/arxiv.2410.16512","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.16512","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tips: Text-image pretraining with spatial awareness.arXiv preprint arXiv:2410.16512","venue":"arXiv (Cornell University)","work_id":"6827f81b-625a-4a2b-9237-e3b2e6978069","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2410.16512","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:4cc995f00b9c94dde19f92212d26ee42e8c2af8f457062afcee5ccda4c8af23f","observation_id":"713e2c36-bf72-4660-9dda-189ac6f17948","resolution":{"observed_at":"2026-05-25T06:20:24.499844Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-06T05:58:29.182448Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":"2304.07193","doi":"10.48550/arxiv.2304.07193","metadata_source":"pith","pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv2: Learning Robust Visual Features without Supervision","venue":"cs.CV","work_id":"26b304e5-b54a-4f26-be7e-83299eca52e4","year":2023},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:033c95ad06ce46a29a85f5ac5df32ca4dcd2a29d34e1737b7f97d025e0cca36a","observation_id":"5e00c399-7a1a-4d46-8f05-3018500b253e","resolution":{"observed_at":"2026-05-25T06:20:24.513102Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.09001","last_updated":"2025-02-26T17:04:31Z","snapshot_observed_at":"2026-07-06T20:21:32.898812Z","submitted_at":"2025-01-15T18:30:58Z","title":"Vision Foundation Models for Computed Tomography","version":2},"cited_work":{"arxiv_id":"2501.09001","doi":"10.48550/arxiv.2501.09001","metadata_source":"arxiv_reference","pith_arxiv_id":"2501.09001","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Vision foundation models for computed tomography","venue":"ArXiv.org","work_id":"91670597-8fc0-475b-90d2-d92140edd40d","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2501.09001","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:a43df9fca6e76e03c1a3126e25b6d5643dea87e8cc4cbf3a32491f0c4c58aea2","observation_id":"0077be65-aedf-46ef-a98e-3f0e86d87db9","resolution":{"observed_at":"2026-05-25T06:20:24.481937Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:7a3e8a4b3742ec8653b8c5ab7b49d80a502fb200f0ace64a3540e25bde7b1ed9","observation_id":"bcd32a76-0396-4986-b084-1fc011a695c6","resolution":{"observed_at":"2026-05-25T06:20:24.469680Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Trends in use of medical imaging in us health care systems and in ontario, canada, 2000-2016","venue":null,"work_id":"7271ad93-8e78-441d-aa1f-09c3e5d63bb7","year":2000},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:b48d68e203722bb432216b365e345fea060ad2bb27efa68d1bcb459d947110bb","observation_id":"224893dc-a9f5-4dda-a747-6aa803b8e652","resolution":{"observed_at":"2026-05-25T10:16:53.589416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.15042","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T16:29:57.816215Z","title":"Com- prehensive language-image pre-training for 3d medical image understanding.arXiv preprint arXiv:2510.15042, 2025a","venue":null,"work_id":"67fe0ca1-0eee-489d-b129-a241a37a6f3f","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:f3a66589d189cf05548e71b57a891adf5750fec8dcb6485fd86c80959f0d3d66","observation_id":"f350a914-2e37-4e6e-b55b-c44c255e56ae","resolution":{"observed_at":"2026-05-25T06:20:24.447345Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":"2505.09388","doi":"10.1016/j.aiopen.2022.12","metadata_source":"pith","pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Technical Report","venue":"cs.CL","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:a976650ae99226785cba70f7a160aa25dd321a9e54d0a0a85f4f56e80c4d8a01","observation_id":"b41f613f-99d6-4936-8a96-5d1aac56e60b","resolution":{"observed_at":"2026-05-25T06:20:24.459129Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00915","last_updated":"2025-01-08T22:58:51Z","snapshot_observed_at":"2026-07-06T14:57:39.647497Z","submitted_at":"2023-03-02T02:20:04Z","title":"BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","version":3},"cited_work":{"arxiv_id":"2303.00915","doi":"10.48550/arxiv.2303.00915","metadata_source":"pith","pith_arxiv_id":"2303.00915","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","venue":"cs.CV","work_id":"6fcf8750-00b8-4f3e-9f0b-965a879a5dff","year":2023},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2303.00915","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:af8602beb1b2bcf37eb31e6b25836dfeec23939771a30ddc41bc95b1953822d0","observation_id":"26e0c7bd-8622-4a2f-b540-f3a75921e502","resolution":{"observed_at":"2026-05-25T06:20:24.429610Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-03T16:39:08.594818+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T16:39:08.594818+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.05176","last_updated":"2025-06-11T02:54:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-05T15:49:48Z","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","version":3},"cited_work":{"arxiv_id":"2506.05176","doi":"10.1016/j.displa.2025.103255","metadata_source":"pith","pith_arxiv_id":"2506.05176","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","venue":"cs.CL","work_id":"bab684a8-d933-426c-a19e-2c855a0d1f59","year":2025},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2506.05176","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:f7da8a3b6a87f8a1f1a6747893f434671885f9bd1345517dd140dfb5e949a629","observation_id":"4e7b9553-73f3-4ecc-be7e-254d0669b806","resolution":{"observed_at":"2026-05-25T06:20:24.435415Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07832","last_updated":"2022-01-27T09:20:49Z","snapshot_observed_at":"2026-07-06T12:08:39.149450Z","submitted_at":"2021-11-15T15:18:05Z","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","version":3},"cited_work":{"arxiv_id":"2111.07832","doi":"10.48550/arxiv.2111.07832","metadata_source":"pith","pith_arxiv_id":"2111.07832","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","venue":"cs.CV","work_id":"ddf5ecec-b36e-4d27-96b7-5aefc236d17c","year":2021},"citing_paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-25T06:16:48.755086Z"},"links":{"cited_paper":"/paper/2111.07832","citing_paper":"/paper/2605.21906"},"observation_digest":"sha256:6c67286435f31a1c3c5a23b54ff58a8fbe49779f3be88ab849cbafce577cc9be","observation_id":"34a1d225-25a5-43d2-a016-95510a46e162","resolution":{"observed_at":"2026-05-25T06:20:24.441028Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-11T01:49:43.656481+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T01:49:43.656481+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.21906","last_updated":"2026-05-22T02:24:20Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-01T00:47:22.134393Z","submitted_at":"2026-05-21T02:28:05Z","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":0,"verified_exact":13,"verified_fuzzy":2},"total_outbound_references":18},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2605.21906."}