{"as_of":"2026-08-10T12:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ac761669d89ce558c0e749b6ed17c8759997c7f2200108a3f3c6c0d6d7b78730","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":"2026-08-01T02:19:58.338496Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2607.25497/citation-record","integrity":"/paper/2607.25497/integrity","json":"/paper/2607.25497/citation-record.json","paper":"/paper/2607.25497"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-01T02:19:58.218424Z","title":"Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.218424Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:3812fb98c83c61f8cdcf2e249f705ae89516b4071c54f9883fbe084048982077","observation_id":"c310b348-00b1-4ab4-aaae-ff53cdb4bde2","resolution":{"observed_at":"2026-08-01T02:19:58.218424Z","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":"2026-08-01T02:19:58.237932Z","title":"Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.Nature Medicine, 28(1):154–163, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.237932Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:805c9b91dd89e436e0cb51f741711052e6cb60ae16d2a953d1c04f44868caf0d","observation_id":"f3da3282-26a2-4549-8e35-7a5d6db3beab","resolution":{"observed_at":"2026-08-01T02:19:58.237932Z","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":"2026-08-01T02:19:58.241374Z","title":"A clinical benchmark of public self-supervised pathology foundation models.Nature Communications, 16(1):3640, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.241374Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:238070bda9918dd5fe772241a114c422903bad851441906e972c39ccef94621b","observation_id":"b12c96d4-2424-478c-a245-e4846aa14a4e","resolution":{"observed_at":"2026-08-01T02:19:58.241374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14294","last_updated":"2021-05-24T17:49:18Z","snapshot_observed_at":"2026-08-04T11:32:10.695202Z","submitted_at":"2021-04-29T12:28:51Z","title":"Emerging Properties in Self-Supervised Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14294","snapshot_observed_at":"2026-08-01T02:19:58.244652Z","title":"Emerging properties in self-supervised vision transformers, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.244652Z"},"links":{"cited_paper":"/paper/2104.14294","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:80fdd908d33b5392028bbeea7c0413d84a8efd886ee04bcac535625417fe6ded","observation_id":"5ac2ed92-43db-4133-9b0d-4dc8b2d44494","resolution":{"observed_at":"2026-08-01T02:19:58.244652Z","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":"2026-08-01T02:19:58.248162Z","title":"Towards a general-purpose foundation model for computational pathology.Nat","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.248162Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:0d65cf3bad741784ae95dec4fcc2471600676d5e977853d44e8504f512cd15c7","observation_id":"2edcd20e-f093-4ceb-a3e9-b508c90c1597","resolution":{"observed_at":"2026-08-01T02:19:58.248162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18055","last_updated":"2025-02-01T09:33:48Z","snapshot_observed_at":"2026-08-10T07:56:52.793310Z","submitted_at":"2025-01-29T23:38:14Z","title":"Current Pathology Foundation Models are unrobust to Medical Center Differences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.18055","snapshot_observed_at":"2026-08-01T02:19:58.251322Z","title":"de Jong, Eric Marcus, and Jonas Teuwen","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.251322Z"},"links":{"cited_paper":"/paper/2501.18055","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:f432d32819b4843a6e70f7b54f4f03c41dfac770941f95e9a94497257b335964","observation_id":"622751fa-50ee-4096-8b49-92e2c782b647","resolution":{"observed_at":"2026-08-01T02:19:58.251322Z","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":"2026-08-01T02:19:58.254822Z","title":"Biased data, biased AI: deep networks predict the acquisition site of TCGA images.Diagn","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.254822Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:b38ffbdd9cdeb15cf93e8ca4aee502c86656745a3f5b2b682475baf69eb65d5c","observation_id":"c5dabece-e315-4776-8c7e-db2e977fbf0e","resolution":{"observed_at":"2026-08-01T02:19:58.254822Z","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":"2026-08-01T02:19:58.257752Z","title":"Wagner, Andrew H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.257752Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:4f6297dc23269b3eb9b58822a7151a05fc609805d92998600f14a851de140001","observation_id":"00457883-66ac-4236-91be-79670ed8bbcf","resolution":{"observed_at":"2026-08-01T02:19:58.257752Z","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":"2026-08-01T02:19:58.260552Z","title":"Distill- ing foundation models for robust and efficient models in digital pathology","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.260552Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:54e7d450bf55769a0ecc14cc5f37d69c7e0f90911c9603d4af26dde347e763ee","observation_id":"a333b67a-aba4-41b0-a84b-4e475a405866","resolution":{"observed_at":"2026-08-01T02:19:58.260552Z","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":"2026-08-01T02:19:58.263327Z","title":"Scaling self-supervised learning for histopathol- ogy with masked image modeling.medRxiv, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.263327Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:45d05c468aba07c00ef576ac7dc3c8806171cfae7540cc4be10ea7706490039c","observation_id":"1ca1a292-31c9-42e3-8a5d-e51b46dae1fa","resolution":{"observed_at":"2026-08-01T02:19:58.263327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09173","last_updated":"2024-09-13T20:12:29Z","snapshot_observed_at":"2026-08-05T01:44:49.087332Z","submitted_at":"2024-09-13T20:12:29Z","title":"Phikon-v2, A large and public feature extractor for biomarker prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.09173","snapshot_observed_at":"2026-08-01T02:19:58.266536Z","title":"Phikon-v2, a large and public feature extractor for biomarker prediction, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.266536Z"},"links":{"cited_paper":"/paper/2409.09173","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:8c4dec6899330ff21376d189f945c92483e7227fec58af6ad79acef2fe2efe0a","observation_id":"c95a2769-f24c-495a-8bca-5da2b1acb191","resolution":{"observed_at":"2026-08-01T02:19:58.266536Z","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":"2026-08-01T02:19:58.269954Z","title":"Shortcut learning in deep neural networks.Nat","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.269954Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:b2b7b3d6e8104a885a64fbc8ab8b9fcac8103a67923f37de274ac547bf8532e0","observation_id":"9172f18c-31b5-4122-b902-3e206e43898f","resolution":{"observed_at":"2026-08-01T02:19:58.269954Z","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":"2026-08-01T02:19:58.273314Z","title":"Deep learning from routine histology improves risk stratification for biochemical re- currence in prostate cancer, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.273314Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:235c44ebd489300d556df9b63ba1fe660513637005a08c191977fc899d73f200","observation_id":"a3a7023e-bbdd-4fed-90d9-9f99a3b09741","resolution":{"observed_at":"2026-08-01T02:19:58.273314Z","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":"2026-08-01T02:19:58.276015Z","title":"The impact of site-specific digital histology signatures on deep learning model accuracy and bias.Nat","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.276015Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:f6eccd7e967507417ce985294fef79a7c17281cfddc1008f2f401ea056c166dd","observation_id":"50aeda5f-a095-4b41-b9c2-97d0538d5b21","resolution":{"observed_at":"2026-08-01T02:19:58.276015Z","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":"2026-08-01T02:19:58.278777Z","title":"Self-Supervised Visual Feature Learning With Deep Neu- ral Networks: A Survey .IEEE Transactions on Pattern Analysis & Machine Intelligence, 43(11):4037–4058, November 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.278777Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:b4de8319f20bb1a4d6349f8ee6ee6080c7e6f51e6b4995662df326f53063c713","observation_id":"77c57b6c-a100-4cc0-940d-797fb6909363","resolution":{"observed_at":"2026-08-01T02:19:58.278777Z","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":"2026-08-01T02:19:58.281483Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.281483Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:3541f65256f40b9771832f3317b6aa7cc7dd813a77dd33a9a282d13a6c63523e","observation_id":"1e9ddaa8-957b-478b-83ae-1abf29c2d211","resolution":{"observed_at":"2026-08-01T02:19:58.281483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05186","last_updated":"2025-04-07T15:38:12Z","snapshot_observed_at":"2026-08-07T16:07:55.268165Z","submitted_at":"2025-04-07T15:38:12Z","title":"Training state-of-the-art pathology foundation models with orders of magnitude less data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05186","snapshot_observed_at":"2026-08-01T02:19:58.284159Z","title":"Training state-of-the-art pathology foundation models with orders of magnitude less data, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.284159Z"},"links":{"cited_paper":"/paper/2504.05186","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:52ab1408ba7e3e05933f5baefb9a6c45c9cb050e82f062f861f6e4e1359819d8","observation_id":"744c3c9c-e6f3-44d1-bdaa-2a5f6652b56e","resolution":{"observed_at":"2026-08-01T02:19:58.284159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.27048","last_updated":"2026-06-22T15:38:22Z","snapshot_observed_at":"2026-08-09T22:14:57.997823Z","submitted_at":"2026-03-27T23:33:33Z","title":"MOOZY: A Patient-First Foundation Model for Computational Pathology","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.27048","snapshot_observed_at":"2026-08-01T02:19:58.287663Z","title":"Hosseini","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.287663Z"},"links":{"cited_paper":"/paper/2603.27048","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:c6e9564396a2fbde6b0fc45f4989a45a56b3f90cdc095bb4c4a83ac1add96b65","observation_id":"9e677715-2603-4c63-b4a0-0dd6ce8ea80c","resolution":{"observed_at":"2026-08-01T02:19:58.287663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.17845","last_updated":"2025-07-22T16:51:53Z","snapshot_observed_at":"2026-08-10T00:51:55.648259Z","submitted_at":"2025-07-22T16:51:53Z","title":"Towards Robust Foundation Models for Digital Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.17845","snapshot_observed_at":"2026-08-01T02:19:58.290831Z","title":"de Jong, Julius Hense, Hannah Marienwald, Jonas Dippel, Philip Naumann, Eric Marcus, Lukas Ruff, Maximilian Alber, Jonas Teuwen, Frederick Klauschen, and Klaus-Robert M¨ uller","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.290831Z"},"links":{"cited_paper":"/paper/2507.17845","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:375e33a196b7496f4c7d750a204be701c5f9f3a13ec03d03fff187916d65b7cc","observation_id":"5f78a882-7163-45d4-83a3-095ed2522f07","resolution":{"observed_at":"2026-08-01T02:19:58.290831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05489","last_updated":"2024-11-08T11:39:03Z","snapshot_observed_at":"2026-08-04T02:53:44.156539Z","submitted_at":"2024-11-08T11:39:03Z","title":"Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05489","snapshot_observed_at":"2026-08-01T02:19:58.294733Z","title":"Do histopathological foundation models eliminate batch effects? a comparative study, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.294733Z"},"links":{"cited_paper":"/paper/2411.05489","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:c3c40491a24242d83adea4fa0767f94cd8cce4b1bd1fb2978f9ed80227c92e2e","observation_id":"1816a462-06ad-4340-83a5-763dff6b2db1","resolution":{"observed_at":"2026-08-01T02:19:58.294733Z","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":"2026-08-01T02:19:58.297872Z","title":"Lu, Bowen Chen, Drew F","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.297872Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:1b584525100de3ae3fd94cb16fee5cff498cff6597de637efab6fefce4fcab97","observation_id":"d8928f50-0daa-4149-bb54-1b67a61232f2","resolution":{"observed_at":"2026-08-01T02:19:58.297872Z","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":"2026-08-01T02:19:58.300773Z","title":"A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature Biomedical Engineering, 10(3):545–564, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.300773Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:9a5812a7c36885310e5dfb73c51cb462a91f52929ca66b911e2d62b2c29d36e2","observation_id":"f5d43742-2ddb-4e7d-a9ab-3b02a4ad1fff","resolution":{"observed_at":"2026-08-01T02:19:58.300773Z","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":"2026-08-01T02:19:58.303782Z","title":"A benchmarking crisis in biomedical machine learning.Nature Medicine, 31(4):1060, April 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.303782Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:fa42cf057a604b9beb31160fa0ac6fce97c9824b859691bb0dc4de4b978bc66c","observation_id":"f722d43c-f563-41f3-94fa-32cef17098e0","resolution":{"observed_at":"2026-08-01T02:19:58.303782Z","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":"2026-08-01T02:19:58.306495Z","title":"MahmoodLab/UNI2-h","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.306495Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:03e2f05a189957d0201f35fd709113447d51346f3620722c62e5b3ba86ab14a4","observation_id":"aec642e5-bfed-4266-b598-8afdbff1839e","resolution":{"observed_at":"2026-08-01T02:19:58.306495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05074","last_updated":"2024-08-20T11:01:21Z","snapshot_observed_at":"2026-08-03T17:13:05.896156Z","submitted_at":"2024-06-07T16:45:53Z","title":"Hibou: A Family of Foundational Vision Transformers for Pathology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05074","snapshot_observed_at":"2026-08-01T02:19:58.309449Z","title":"Hibou: a family of foundational vision transformers for pathology, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.309449Z"},"links":{"cited_paper":"/paper/2406.05074","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:aeeb5e8cae077e0c67309a7b395602078cc0227b81c18996022367dc03209681","observation_id":"fa18bd6d-d27e-41ac-824e-4cf83c0b7b3c","resolution":{"observed_at":"2026-08-01T02:19:58.309449Z","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":"2026-08-01T02:19:58.312259Z","title":"Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nat","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.312259Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:75b5422232009a7793392cc55154ba5a2151504db4600abec9bf12548f56d614","observation_id":"d6c97bc8-924f-4bf0-8f19-095b4f3215be","resolution":{"observed_at":"2026-08-01T02:19:58.312259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-09T18:02:17.307812Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-01T02:19:58.314970Z","title":"DINOv2: Learning robust visual features without supervision, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.314970Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:d4a9306efadaf0542b326a383b4ae827bf2d019e720df2334e3bd381ec5f73b6","observation_id":"ec10f019-1dac-4c74-854c-8a42a6d4862e","resolution":{"observed_at":"2026-08-01T02:19:58.314970Z","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":"2026-08-01T02:19:58.318459Z","title":"H-optimus-0.https://github.com/bioptimus/releases/ tree/main/models/h-optimus/v0, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.318459Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:4a2fd994470456c0e89c9bbe75233f31a62e875bfebbe7d04796ef44520157fc","observation_id":"28e89bda-f9a0-492b-b2b1-ec2485528581","resolution":{"observed_at":"2026-08-01T02:19:58.318459Z","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":"2026-08-01T02:19:58.321325Z","title":"Mariet, and Rodolphe Jenatton","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.321325Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:8a0006188ad4f1b08ee715a578422258acfcad3ffa6d62ec4c2edf84f0f3a65e","observation_id":"86175b6b-83e3-43a9-bd33-d9914c202f42","resolution":{"observed_at":"2026-08-01T02:19:58.321325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10254","last_updated":"2024-05-22T17:22:32Z","snapshot_observed_at":"2026-08-10T00:50:57.116457Z","submitted_at":"2024-05-16T16:59:12Z","title":"PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10254","snapshot_observed_at":"2026-08-01T02:19:58.324062Z","title":"Kunz, Juan A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.324062Z"},"links":{"cited_paper":"/paper/2405.10254","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:2819acddd49a965527347c0aa3dcc525e7f13c1651ad1f85e2918465023f0d79","observation_id":"a882806a-0791-44b9-a05f-e3b822301859","resolution":{"observed_at":"2026-08-01T02:19:58.324062Z","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":"2026-08-01T02:19:58.327197Z","title":"A foundation model for clinical-grade computational pathology and rare cancers detection.Nat","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.327197Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:b40b05cd0a4955da093de21db39895436b44c185f69e6996a74c57325eacb857","observation_id":"3b826fa3-921a-4843-870b-88d8ce99ce79","resolution":{"observed_at":"2026-08-01T02:19:58.327197Z","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":"2026-08-01T02:19:58.329888Z","title":"Nirschl, Joel Neal, Maximilian Diehn, Sen Yang, and Rui- jiang Li","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.329888Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:4452c00d289d8c111405a7c51a91de5ee608e81b5d690fe6d54a8f86c18a4e5b","observation_id":"e51a52d5-3ba1-4eca-a3a0-20ccd9ae6c6b","resolution":{"observed_at":"2026-08-01T02:19:58.329888Z","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":"2026-08-01T02:19:58.332879Z","title":"A whole-slide foundation model for digital pathology from real-world data.Nature, 630(8015):181–188, June 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.332879Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:8ace57ce9daf4a0b91c8d92be5551d6147875f607e7eca42da882ea291c412e9","observation_id":"998831ca-b8cb-4865-96ba-b1fe8497811f","resolution":{"observed_at":"2026-08-01T02:19:58.332879Z","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":"2026-08-01T02:19:58.335644Z","title":"A multi- modal knowledge-enhanced whole-slide pathology foundation model.Nature Communications, 16(1):11406, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.335644Z"},"links":{"citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:ba1160676afa4d3d672fdd7b03d9ba84caaeae183ad6971b73985640a8150dde","observation_id":"05279c4c-b867-465d-9247-9ab8b8f399c6","resolution":{"observed_at":"2026-08-01T02:19:58.335644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00738","last_updated":"2024-11-06T14:45:58Z","snapshot_observed_at":"2026-08-02T04:10:01.034108Z","submitted_at":"2024-08-01T17:35:58Z","title":"Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00738","snapshot_observed_at":"2026-08-01T02:19:58.338496Z","title":"Virchow2: Scaling self-supervised mixed magnification models in pathology, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T02:19:58.338496Z"},"links":{"cited_paper":"/paper/2408.00738","citing_paper":"/paper/2607.25497"},"observation_digest":"sha256:2caee5bb33c5db1825fde39999ce175a4def3bbebabd81e937ca5dd9d84cb624","observation_id":"400efdc6-f583-4d5d-a7f3-5d50c4150ef1","resolution":{"observed_at":"2026-08-01T02:19:58.338496Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.25497","last_updated":"2026-07-28T09:34:30Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T19:28:37.227105Z","submitted_at":"2026-07-28T09:34:30Z","title":"Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.25497."}