{"as_of":"2026-08-09T00:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a746325402f23586814aae818aaf742bf65e2adedc6dab3b7d4c7f9c32629f5","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:48:10.417655Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2507.17662/citation-record","integrity":"/paper/2507.17662/integrity","json":"/paper/2507.17662/citation-record.json","paper":"/paper/2507.17662"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:48:11.524319Z","title":"Global Cancer Statistics 2020: Globocan Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,","venue":null,"work_id":"c50ad627-7033-4768-9f25-e96ce9ab31f4","year":2020},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.134796Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:fa4714b0193c968380d072118d1883090b2f1893bedb2bf92e7e7fa6663b9813","observation_id":"8f9105b5-b6f0-4731-aae8-fa810c087720","resolution":{"observed_at":"2026-08-06T14:48:11.535577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.488762Z","title":"Novel use of Scout® Reflector for Target Localization in Preoperative Breast Radiotherapy,","venue":null,"work_id":"0f623ceb-aff3-4398-9a08-8bfb8b70f479","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.140885Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:d3b4bafacab98f4f3d8073dfeacd94267814de7b76d7e6861e96e8635f11b13b","observation_id":"20cb5cbc-1fbb-4b64-9472-30e054beb53f","resolution":{"observed_at":"2026-08-06T14:48:11.500625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.443030Z","title":"Po91: Evaluation of Advanced Collapsed Cone Engine (ACE) Treatment Planning Algorithm for HDR Breast Plans,","venue":null,"work_id":"c8a92010-c4c9-4033-a3ea-b964e732e647","year":2023},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.151012Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:40ed936093cb6a5ff888af9fa9bc5f5b832f634e33fd086792e2a3d3dfe00db5","observation_id":"ef045f18-9e29-49d7-8f68-2256aa842b81","resolution":{"observed_at":"2026-08-06T14:48:11.452208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.424837Z","title":"A Novel Method for Measuring the Burden of Breast Cancer in Neighborhoods,","venue":null,"work_id":"39b67cb6-559c-4c42-8cde-333a0cc63d57","year":2023},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.159154Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:6ad6d60dcb8bdca034b01c5e8ef5c05731bd0e66e9b6d09254ea3bb3bc94e21f","observation_id":"6510dfd9-0dbb-4b07-a87d-9382a38f9d32","resolution":{"observed_at":"2026-08-06T14:48:11.431200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.385834Z","title":"Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening,","venue":null,"work_id":"7dc7fd71-00d3-42fb-815e-f452d8591824","year":2005},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.168501Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:2425f4c5b8f17000247eecdb329c1a9ceb57ca01af4111f6635d378defd60a5f","observation_id":"9342fdf1-44cd-4852-91f8-106adf2afab4","resolution":{"observed_at":"2026-08-06T14:48:11.391208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.351013Z","title":"A review of computer aided detection in mammography,","venue":null,"work_id":"d3b90fe6-488b-4277-b7c8-1f27136afc36","year":2018},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.175865Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:eaabf75c3674040fff2b129621750f7d49f0b35c8a8cbe961aa10c6de1ca39a9","observation_id":"2a6d8f0a-a32f-48ac-8588-372aa30c08fd","resolution":{"observed_at":"2026-08-06T14:48:11.369615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.313450Z","title":"Multi-view convolu- tional neural networks for mammographic image classification,","venue":null,"work_id":"14c316a9-226c-4739-8e13-4daf17a40c3a","year":2019},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.187328Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:e39c8c74b9e9189fb911c90cc248551343ec425f0e766664ef0dc78fcfad5fb9","observation_id":"960780bd-69a1-4fb9-919e-a44880293254","resolution":{"observed_at":"2026-08-06T14:48:11.325409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.286816Z","title":"Multi-view feature fusion based four views model for mammo- gram classification using convolutional neural network,","venue":null,"work_id":"3c603315-a780-4a8d-b61f-83fadf4226ea","year":2019},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.199737Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:ee0f7c5fa5f2e7bf18f40092111281e6fb67acf5ef5a0f9577a055ee7a9a11c0","observation_id":"3bbb0d42-6644-448b-a4c7-73313ecdae77","resolution":{"observed_at":"2026-08-06T14:48:11.296301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.254745Z","title":"Deep learning to improve breast cancer detection on screening mammography,","venue":null,"work_id":"5a6816cd-76ca-4b47-954c-f7f330a9da9d","year":2019},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.206146Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:7dd9c7ddf8c11c766d5cebc37d0da38b4bcaba91e3ad50b111538b7e90d67570","observation_id":"0598f24c-d35f-4f45-bfc0-b3c5903496d2","resolution":{"observed_at":"2026-08-06T14:48:11.265268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.229307Z","title":"An in- tegrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks,","venue":null,"work_id":"04a08056-a750-48d8-ac43-65b786e8b859","year":2022},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.211713Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:c9740f84e3c3dce48525fb7bf7ed8011402d2ca569fc022a8addb9df68a03371","observation_id":"7d5e839c-9138-45a6-8736-b4fe2824ada3","resolution":{"observed_at":"2026-08-06T14:48:11.235432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.203012Z","title":"Mv-swin-t: mam- mogram classification with multi-view swin transformer,","venue":null,"work_id":"2d4d0fa8-c824-4272-ad40-67d3209460ad","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.216984Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:2cf166c8707b121de18e30802add8856006b2aec6a75f6868d6e011ff625e2bc","observation_id":"998a6d5b-25c5-4dde-948e-735b1644010a","resolution":{"observed_at":"2026-08-06T14:48:11.210189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.148499Z","title":"Integrating ai for human-centric breast cancer diagnostics: A multi- scale and multi-view swin transformer framework,","venue":null,"work_id":"04600e6e-d695-42ec-8c83-246607ca9603","year":2025},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.225500Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:a94119379c13a51d6fca2e5bdb750a258218a9b81950550727db00669f4bd75b","observation_id":"e4fcd3c7-2453-4332-975b-07c4ba023a1f","resolution":{"observed_at":"2026-08-06T14:48:11.166609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.108730Z","title":"Advancing precision in breast can- cer detection: a fusion of vision transformers and cnns for calcification mammography classification,","venue":null,"work_id":"c6424bb5-a695-4b2f-94cb-5b46b2f5ee3f","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.237823Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:8260726b2c3a1939d5567412a21af05325248f9ebc234308a2ec06c54487bd5a","observation_id":"7cc3cdc9-6b4c-4d63-a330-e0f442975e8f","resolution":{"observed_at":"2026-08-06T14:48:11.123792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:11.074978Z","title":"Hybridmammonet: A hybrid cnn-vit architecture for multi-view mammography image classifica- tion,","venue":null,"work_id":"977e9ad5-e19c-4cbf-a112-9fd911fb972e","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.253523Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:b0b5e876b3084b51156d773c7175b3a5f2f41b2a2ec7dd84bd5a14dbd06900da","observation_id":"a313530f-cc81-4b92-afc5-4c8170ba91a5","resolution":{"observed_at":"2026-08-06T14:48:11.085332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-06T14:48:10.260857Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.260857Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:ecefb3e0065fe82be4eb110c21b6e14004172096ebc1d7fc12df3c354adc4a5b","observation_id":"81357111-dbdb-453b-8b17-ef68a93780af","resolution":{"observed_at":"2026-08-06T14:48:10.260857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-07-06T17:16:59.193820Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-06T14:48:10.267720Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.267720Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:a18fc3a85a485928ea0a0be251dcd01219bba76a2d5044bb64212349ca96823a","observation_id":"70220b68-1fa2-45fe-9880-9bec621e0bd2","resolution":{"observed_at":"2026-08-06T14:48:10.267720Z","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":"2026-08-06T14:48:10.986606Z","title":"Vmamba: Visual state space model,","venue":null,"work_id":"8987fbb9-1bee-47d6-a23a-952aef96bbc3","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.276797Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:9cc41ad6385f94a24459a96995c482cfe21429f2fec506a93aa8433ca3befa41","observation_id":"8656036d-f036-4226-b14b-b3a00a461792","resolution":{"observed_at":"2026-08-06T14:48:11.013848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:10.939498Z","title":"Multi-scale vmamba: Hierarchy in hierarchy visual state space model,","venue":null,"work_id":"6831b9ae-4eb9-4614-a32d-e0b5c924a1f4","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.284986Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:e9b5ff89efd86840536864621f651524a47a092b0bb73471f71bee85cd4af5f3","observation_id":"a23edc76-839f-48eb-91f0-de70d26f9a97","resolution":{"observed_at":"2026-08-06T14:48:10.959919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03849","last_updated":"2024-09-29T03:55:46Z","snapshot_observed_at":"2026-08-04T18:38:48.993581Z","submitted_at":"2024-03-06T16:49:33Z","title":"MedMamba: Vision Mamba for Medical Image Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03849","snapshot_observed_at":"2026-08-06T14:48:10.297988Z","title":"Medmamba: Vision mamba for medical image classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.297988Z"},"links":{"cited_paper":"/paper/2403.03849","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:14774965c8a77890438e4fdf973274508016c7207a286acb766727fb1cf96e6d","observation_id":"6b7f2eb0-8acb-4492-9ddf-43debe5a2cbb","resolution":{"observed_at":"2026-08-06T14:48:10.297988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04787","last_updated":"2025-04-07T07:31:28Z","snapshot_observed_at":"2026-08-07T16:08:05.455183Z","submitted_at":"2025-04-07T07:31:28Z","title":"Dynamic Vision Mamba","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04787","snapshot_observed_at":"2026-08-06T14:48:10.309469Z","title":"Dynamic vision mamba,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.309469Z"},"links":{"cited_paper":"/paper/2504.04787","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:62b02366cbff5d753754c684347f515b0f8e9244b1524d649ebfbec332f09a47","observation_id":"a703c0a3-14fc-43c5-944c-21c4ceb7afab","resolution":{"observed_at":"2026-08-06T14:48:10.309469Z","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":"2026-08-06T14:48:10.917016Z","title":"Mambavision: A hybrid mamba- transformer vision backbone,","venue":null,"work_id":"7f323f98-0285-462f-b683-ae3623ada7d4","year":2025},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.330622Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:0f7c83176e1b9706736907ce908dcaf3b9e9421a2cc8e3ab2c2d46e18fcc5397","observation_id":"6fdcb170-9b0c-452d-b1a4-2dcbbfec51e4","resolution":{"observed_at":"2026-08-06T14:48:10.924505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:48:10.338124Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.338124Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:dfd357fe01200b8f54bce0cdb73b449ae7829e5a3645ead7635d30ec029e90f8","observation_id":"ab109ef0-1aa8-4ed9-a8d2-88e544a88188","resolution":{"observed_at":"2026-08-06T14:48:10.338124Z","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":"2026-08-06T14:48:10.877470Z","title":"A curated mammography data set for use in computer-aided detection and diagnosis research,","venue":null,"work_id":"3d4ac797-99c2-4387-a828-b1eacf9f3dbb","year":2017},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.344919Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:41041d881db0ccb252cccb697dc16c5637b654affab070b6072e6050bf7d2f43","observation_id":"00e4f446-bbfd-482d-9f6a-0001abf0aa72","resolution":{"observed_at":"2026-08-06T14:48:10.883371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17727","last_updated":"2025-02-24T23:41:33Z","snapshot_observed_at":"2026-08-07T17:51:41.395052Z","submitted_at":"2025-02-24T23:41:33Z","title":"Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?","version":1},"cited_work":{"arxiv_id":"2502.17727","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.17727","snapshot_observed_at":"2026-08-06T14:48:10.581546Z","title":"Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?","venue":"cs.CV","work_id":"8e564d29-7856-483d-81fe-b5e2495f724f","year":2025},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.349836Z"},"links":{"cited_paper":"/paper/2502.17727","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:b8ffb13bf0357ba9a5007b83444bca97fe9abe4f9780d1eba7620c6ef2eef774","observation_id":"857236c3-2b9d-4394-9810-8e443b7d5783","resolution":{"observed_at":"2026-08-06T14:48:10.590275Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:48:10.361326Z","title":"Exploiting patch sizes and resolutions for multi-scale deep learning in mammogram image classification,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.361326Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:72aed34a60c354bc17a4650229db89eefeb2defc156d687add8a6724f16943b1","observation_id":"64ab96b8-8340-4d51-a490-5d814b830b32","resolution":{"observed_at":"2026-08-06T14:48:10.361326Z","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":"2026-08-06T14:48:10.811364Z","title":"Deep cross-training: An approach to improve deep neural network classification on mam- mographic images,","venue":null,"work_id":"58984e7b-480c-46f0-b035-bb119d98c252","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.371244Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:d1efb2ea5a48ad074ae67a30b6926f194ae0561d24dc17c2cedb5c65d6664af9","observation_id":"ae38d27b-7030-4380-b21d-2f3c0654d5ee","resolution":{"observed_at":"2026-08-06T14:48:10.819080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14876","last_updated":"2025-03-15T07:30:53Z","snapshot_observed_at":"2026-07-06T19:20:19.151388Z","submitted_at":"2024-09-23T10:17:13Z","title":"Mammo-Clustering: A Multi-views Tri-level Information Fusion Context Clustering Framework for Localization and Classification in Mammography","version":4},"cited_work":{"arxiv_id":"2409.14876","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.14876","snapshot_observed_at":"2026-08-06T14:48:10.526886Z","title":"Mammo-Clustering: A Multi-views Tri-level Information Fusion Context Clustering Framework for Localization and Classification in Mammography","venue":"cs.CV","work_id":"74472d79-6a33-4052-838a-67d5cfdd6825","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.386907Z"},"links":{"cited_paper":"/paper/2409.14876","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:609358426251de3883daecb247ba83c394a523654aa0d0773a0e8ee9206ae6e4","observation_id":"8f75b13f-ad7e-4920-b2a9-01df2bdddd20","resolution":{"observed_at":"2026-08-06T14:48:10.539024Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:48:10.782791Z","title":"An open codebase for enhancing transparency in deep learning-based breast cancer diagnosis utilizing cbis-ddsm data,","venue":null,"work_id":"a8a500f1-09fe-497e-b2f6-ea508680a460","year":2024},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.403153Z"},"links":{"citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:f54acd00c243e3d5c16b9c47c34b9681ec55575401b2f9a8eea88a9e8ce88a5b","observation_id":"3beabb78-4d89-4d7d-998a-3a266bfa2bf3","resolution":{"observed_at":"2026-08-06T14:48:10.793097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02619","last_updated":"2025-03-04T13:38:58Z","snapshot_observed_at":"2026-08-07T17:30:25.250985Z","submitted_at":"2025-03-04T13:38:58Z","title":"XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02619","snapshot_observed_at":"2026-08-06T14:48:10.417655Z","title":"Xfmamba: Cross-fusion mamba for multi-view medical image classification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T14:48:10.417655Z"},"links":{"cited_paper":"/paper/2503.02619","citing_paper":"/paper/2507.17662"},"observation_digest":"sha256:205611f8949f12288f32baf45249623239cf1fda1a294261e2344c949f52245f","observation_id":"5458392e-e97a-4de0-b819-ec805dd9bdd2","resolution":{"observed_at":"2026-08-06T14:48:10.417655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.17662","last_updated":"2025-07-23T16:29:46Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-07T00:48:28.295183Z","submitted_at":"2025-07-23T16:29:46Z","title":"Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":20},"total_outbound_references":29},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.17662."}