{"as_of":"2026-08-14T15:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12e2962d3b7bed024a84ac4083ab882b9cc2684d4a66a17886a7accd89d99f95","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:18:27.030770Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.14473/citation-record","integrity":"/paper/2412.14473/integrity","json":"/paper/2412.14473/citation-record.json","paper":"/paper/2412.14473"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.04906","last_updated":"2022-01-28T12:23:37Z","snapshot_observed_at":"2026-08-12T14:02:29.797842Z","submitted_at":"2021-05-11T09:53:21Z","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04906","snapshot_observed_at":"2026-08-11T12:18:26.624400Z","title":"arXiv:2105.04906","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.624400Z"},"links":{"cited_paper":"/paper/2105.04906","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:d4b0849cae04ffee9ba865557e23444efedee9216720a228f9293491f5e31f54","observation_id":"76a8f1ff-9cbe-4fc7-b34a-aaa257b54225","resolution":{"observed_at":"2026-08-11T12:18:26.624400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04297","last_updated":"2020-03-09T17:56:49Z","snapshot_observed_at":"2026-07-06T09:03:25.467987Z","submitted_at":"2020-03-09T17:56:49Z","title":"Improved Baselines with Momentum Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04297","snapshot_observed_at":"2026-08-11T12:18:26.728724Z","title":"In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16144–16155","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.728724Z"},"links":{"cited_paper":"/paper/2003.04297","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:1672a4f2f785bd2c8d3e8054b50ada7f99cffc1cb0020b3b3905f3d4fe27fc1f","observation_id":"e01b6f27-60aa-4d4b-bd07-4f0dcc9b9420","resolution":{"observed_at":"2026-08-11T12:18:26.728724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T14:19:26.598265Z","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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-11T12:18:26.744267Z","title":"arXiv:2010.11929","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.744267Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:d549edcddf1e202459a66dd73956f3bd4af2d04820d1fb3be321b41eb516a6e3","observation_id":"45092a22-9917-4e4f-aeca-e0af20d76bac","resolution":{"observed_at":"2026-08-11T12:18:26.744267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:18:27.638561Z","title":"In Medical Image Computing and Com- puter Assisted Intervention–MICCAI 2022: 25th Interna- tional Conference, Singapore, September 18–22, 2022, Pro- ceedings, Part II, 35–45","venue":null,"work_id":"3fceb992-017b-4955-8bd0-dfc719838538","year":2022},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.759009Z"},"links":{"citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:3f6f6bf971ea2b4070f26c9f491c45434340ea99a4eb0bf289c2df2e8d19e1da","observation_id":"0828be3b-5512-4136-9bcc-914f6af4fd1b","resolution":{"observed_at":"2026-08-11T12:18:27.645476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17013","last_updated":"2022-10-31T02:06:39Z","snapshot_observed_at":"2026-08-14T07:00:41.904589Z","submitted_at":"2022-10-31T02:06:39Z","title":"Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images","version":1},"cited_work":{"arxiv_id":"2210.17013","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.17013","snapshot_observed_at":"2026-08-11T12:18:27.100205Z","title":"Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images","venue":"cs.CV","work_id":"e96a7cb2-6841-42ac-bf42-69be65800ec7","year":2022},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.764930Z"},"links":{"cited_paper":"/paper/2210.17013","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:34a13cf01abdf7ead103d0342954e17ce1224f9dc5ceab8b794a7ca4f3318788","observation_id":"a155966b-509f-4112-8bb4-704d00c77552","resolution":{"observed_at":"2026-08-11T12:18:27.147956Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:18:27.616492Z","title":"In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, 3457–3466","venue":null,"work_id":"ddeef943-40cf-4771-aab9-517d05d1b3b3","year":2022},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.807137Z"},"links":{"citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:fff2580b48015c5ef608e943339bfea8e33c33fd62adf5f2df34d9f3f3c4448d","observation_id":"12643ed5-2ad2-4c8d-a70c-ff42be71e4b9","resolution":{"observed_at":"2026-08-11T12:18:27.623867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07832","snapshot_observed_at":"2026-08-11T12:18:26.871889Z","title":"arXiv:2111.07832","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.871889Z"},"links":{"cited_paper":"/paper/2111.07832","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:d01e93c0fc3d741660d47c0e2db24f3a17c1d1845eff421a2c75fca81a3666be","observation_id":"c4960493-fd7d-4c4c-a022-427d7a08820c","resolution":{"observed_at":"2026-08-11T12:18:26.871889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:18:27.585542Z","title":"This strategy generates 457,000, 422,000, and 1,839,000 patches, respectively for USTC-EGFR, TCGA- EGFR, and TCGA-LUNG-3K dataset, for the training of the self-supervised models","venue":null,"work_id":"ca1282c4-3b88-4e09-99f0-1e8cb9279f9c","year":2021},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.957339Z"},"links":{"citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:f4cfe663c67228e0f8d8a4568f5341a1157f2599dff5f0c506a761b3100a60b9","observation_id":"437a0408-31e7-4a59-a95a-1cfa1a720b30","resolution":{"observed_at":"2026-08-11T12:18:27.602846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05908","last_updated":"2021-01-03T16:56:46Z","snapshot_observed_at":"2026-08-10T10:13:44.536180Z","submitted_at":"2016-06-19T21:02:30Z","title":"Tutorial on Variational Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05908","snapshot_observed_at":"2026-08-11T12:18:26.737612Z","title":"arXiv:1606.05908","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.737612Z"},"links":{"cited_paper":"/paper/1606.05908","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:99a3450f7178083d01084b18e68ffe3f9c503a29d03cbb5dbd019040404c5c86","observation_id":"b773e7aa-38f6-4b10-bc92-157b6aba00da","resolution":{"observed_at":"2026-08-11T12:18:26.737612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08254","last_updated":"2022-09-03T14:11:33Z","snapshot_observed_at":"2026-08-13T16:48:25.566954Z","submitted_at":"2021-06-15T16:02:37Z","title":"BEiT: BERT Pre-Training of Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08254","snapshot_observed_at":"2026-08-11T12:18:26.560604Z","title":"arXiv:2106.08254","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.560604Z"},"links":{"cited_paper":"/paper/2106.08254","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:5d87a97ffb51248f9c5bbfe9b88cece69258fd9f6db261052dc21bdf13697d9e","observation_id":"eb5f2a65-d6f9-4015-b30b-5440e14621ff","resolution":{"observed_at":"2026-08-11T12:18:26.560604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.03679","last_updated":"2023-03-07T06:38:48Z","snapshot_observed_at":"2026-08-13T12:30:08.364998Z","submitted_at":"2023-03-07T06:38:48Z","title":"MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors","version":1},"cited_work":{"arxiv_id":"2303.03679","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.03679","snapshot_observed_at":"2026-08-11T12:18:27.229126Z","title":"MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors","venue":"cs.LG","work_id":"c82819e3-a59e-4a38-bb53-7667ee67b9a6","year":2023},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:26.751253Z"},"links":{"cited_paper":"/paper/2303.03679","citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:b6f9ba94c876c203c7accfda99ebb7e0d491e47b89bda9d6f2306dfe51dd6f6d","observation_id":"fef87141-a28a-4d19-8776-9a70236459d7","resolution":{"observed_at":"2026-08-11T12:18:27.318976Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:18:27.444909Z","title":"However, the AUC of CONCH is 21.6% lower than our method in the TCGA- EGFR dataset","venue":null,"work_id":"e3212e42-6ea9-4195-8019-b5f9b653fe5a","year":2024},"citing_paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T12:18:27.030770Z"},"links":{"citing_paper":"/paper/2412.14473"},"observation_digest":"sha256:adc2cb3848fa69507a111f98f8ef176c03b51a7c7a23759fc396b694f4936601","observation_id":"04dac8a5-f572-413e-8862-cb3d94772f60","resolution":{"observed_at":"2026-08-11T12:18:27.497117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.14473","last_updated":"2024-12-19T02:47:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T19:15:56.681865Z","submitted_at":"2024-12-19T02:47:17Z","title":"Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":2,"verified_fuzzy":4},"total_outbound_references":12},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2412.14473."}