{"as_of":"2026-08-07T05:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4fb7480b02330e4604132347a0a68eb386cf2d8187c9638d18974114f1cd69ae","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:36:00.434728Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T12:42:18.528289Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.00722","last_updated":"2024-09-30T04:23:54Z","snapshot_observed_at":"2026-08-02T17:57:34.542089Z","submitted_at":"2024-01-01T10:49:09Z","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":"2401.00722","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.00722","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6e9c8cc9-0769-4cd9-b9f8-87fb1b65398a","year":2024},"citing_paper":{"arxiv_id":"2505.18823","last_updated":"2026-04-22T09:32:13Z","snapshot_observed_at":"2026-08-03T05:54:16.738050Z","submitted_at":"2025-05-24T18:48:29Z","title":"MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-19T12:40:31.426026Z"},"links":{"cited_paper":"/paper/2401.00722","citing_paper":"/paper/2505.18823"},"observation_digest":"sha256:854a9e03cf9f1cd64458eb807aefa9935c3fb0d02a559315ee42f9c68ddb06d9","observation_id":"5c4c2b4d-9cc2-44c2-8cfa-e4e5a8ea4a19","resolution":{"observed_at":"2026-05-19T12:42:18.530580Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00722","last_updated":"2024-09-30T04:23:54Z","snapshot_observed_at":"2026-08-02T17:57:34.542089Z","submitted_at":"2024-01-01T10:49:09Z","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00722","snapshot_observed_at":"2026-08-06T20:36:00.434728Z","title":"Brau-net++: U-shaped hybrid cnn-transformer network for medical image segmenta- tion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-06T20:25:46.698801Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.434728Z"},"links":{"cited_paper":"/paper/2401.00722","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:2ec89c5ab734576d61470a3ee47ff542c4c68c0bd23e899ff28ccfb20358342f","observation_id":"a35a9d08-00e8-43dd-9d07-6abc86cf2ea1","resolution":{"observed_at":"2026-08-06T20:36:00.434728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00722","last_updated":"2024-09-30T04:23:54Z","snapshot_observed_at":"2026-08-02T17:57:34.542089Z","submitted_at":"2024-01-01T10:49:09Z","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00722","snapshot_observed_at":"2026-08-06T15:33:58.973764Z","title":"arXiv preprint arXiv:2401.00722 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15595","last_updated":"2025-07-21T13:18:05Z","snapshot_observed_at":"2026-08-06T15:25:46.807353Z","submitted_at":"2025-07-21T13:18:05Z","title":"SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:33:58.973764Z"},"links":{"cited_paper":"/paper/2401.00722","citing_paper":"/paper/2507.15595"},"observation_digest":"sha256:2206771ca421217f68273c33c3a7a4181cd385f51eff093b26b10dc62a04839c","observation_id":"befeee64-e4df-47b7-b749-c7ffaab8e8ba","resolution":{"observed_at":"2026-08-06T15:33:58.973764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00722","last_updated":"2024-09-30T04:23:54Z","snapshot_observed_at":"2026-08-02T17:57:34.542089Z","submitted_at":"2024-01-01T10:49:09Z","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00722","snapshot_observed_at":"2026-08-06T01:01:32.660023Z","title":"Brau- net++: U-shaped hybrid cnn-transformer network for medical image segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04058","last_updated":"2025-08-06T03:38:07Z","snapshot_observed_at":"2026-08-06T01:01:29.353844Z","submitted_at":"2025-08-06T03:38:07Z","title":"TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T01:01:32.660023Z"},"links":{"cited_paper":"/paper/2401.00722","citing_paper":"/paper/2508.04058"},"observation_digest":"sha256:2b7a3407bd4e3df38faf72b8bcbe48783e8543f4aabdff5431d0512433c88a90","observation_id":"f489fbbb-07d5-4323-935f-d7bfe7dec276","resolution":{"observed_at":"2026-08-06T01:01:32.660023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.00722/citation-record","integrity":"/paper/2401.00722/integrity","json":"/paper/2401.00722/citation-record.json","paper":"/paper/2401.00722"},"outbound":[],"paper":{"arxiv_id":"2401.00722","last_updated":"2024-09-30T04:23:54Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-02T17:57:34.542089Z","submitted_at":"2024-01-01T10:49:09Z","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2401.00722."}