{"as_of":"2026-08-23T07:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:11f2a87b62acbf72f8724dba52b7b7c5f5a2f3d2dcb767b94859c14755fa929a","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":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":25,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:55:02.023484Z","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":"2026-07-04T09:49:44.637637Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":"2401.14168","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-04T09:49:44.637637Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":"0d4a532a-99aa-4b6a-b1a0-53c459b0999e","year":2024},"citing_paper":{"arxiv_id":"2312.06635","last_updated":"2024-08-27T01:27:29Z","snapshot_observed_at":"2026-08-21T01:26:32.098232Z","submitted_at":"2023-12-11T18:51:59Z","title":"Gated Linear Attention Transformers with Hardware-Efficient Training","version":6},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-05-15T01:15:13.991219Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2312.06635"},"observation_digest":"sha256:f672c2bff50e2c7238b74c8eeb0cde4109e5063a95f1d4241774f200017ed88a","observation_id":"6000aad7-27c6-480d-82c9-c83cc2cd730b","resolution":{"observed_at":"2026-05-15T01:15:14.214459Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":"2401.14168","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-04T09:49:44.637637Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":"0d4a532a-99aa-4b6a-b1a0-53c459b0999e","year":2024},"citing_paper":{"arxiv_id":"2404.07106","last_updated":"2026-05-19T13:59:07Z","snapshot_observed_at":"2026-08-12T19:59:08.323419Z","submitted_at":"2024-04-10T15:45:03Z","title":"3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-24T02:09:11.515173Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2404.07106"},"observation_digest":"sha256:03b2a7fb2cb0f2b5d294ec892b0fdcc7733a34ab9e07d1001e9a6b812a5fa709","observation_id":"e8521388-e851-438e-a95e-67a8515f073f","resolution":{"observed_at":"2026-05-24T02:13:45.187320Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":"2401.14168","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-04T09:49:44.637637Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":"0d4a532a-99aa-4b6a-b1a0-53c459b0999e","year":2024},"citing_paper":{"arxiv_id":"2408.01129","last_updated":"2026-04-06T02:10:55Z","snapshot_observed_at":"2026-08-11T00:37:21.988846Z","submitted_at":"2024-08-02T09:18:41Z","title":"A Survey of Mamba","version":8},"reference_index":217,"source":"pdf_text","source_observed_at":"2026-05-23T22:09:19.917854Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2408.01129"},"observation_digest":"sha256:59a99d68ad645b34cc63c972d34c9c1745bd9839fc897f8c78344e6d4fead4a7","observation_id":"4d398b0d-02f3-49c6-9055-e81aee8aacd7","resolution":{"observed_at":"2026-05-23T22:13:30.977912Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-12T18:16:41.751922Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11717","last_updated":"2024-11-18T16:45:44Z","snapshot_observed_at":"2026-08-20T09:04:36.833880Z","submitted_at":"2024-11-18T16:45:44Z","title":"RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T18:16:41.751922Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2411.11717"},"observation_digest":"sha256:44b7681b92e0aeb4f72076edb17bc65429c37c8c441d632c0095c5677174dc87","observation_id":"ee15573d-bd33-442c-94d3-134dce048f39","resolution":{"observed_at":"2026-08-12T18:16:41.751922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-12T13:06:35.556109Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16481","last_updated":"2025-03-11T16:05:43Z","snapshot_observed_at":"2026-08-19T00:10:21.032978Z","submitted_at":"2024-11-25T15:21:48Z","title":"Deformable Mamba for Wide Field of View Segmentation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:35.556109Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2411.16481"},"observation_digest":"sha256:aba9b0338aa01f9fd83542a852cb4ce93a2f75b3ebd38d21ac208cc25b0ede54","observation_id":"63221f28-e180-41c2-a065-bb5c0580232a","resolution":{"observed_at":"2026-08-12T13:06:35.556109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-11T14:07:16.290069Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.12496","last_updated":"2025-04-14T09:37:17Z","snapshot_observed_at":"2026-08-19T13:19:25.397938Z","submitted_at":"2024-12-17T02:56:35Z","title":"Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training","version":4},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T14:07:16.290069Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2412.12496"},"observation_digest":"sha256:dfab0acfe10700aab98d71e5b8b6cfdbf3f9731a0edafd5ad3dc8885baba9987","observation_id":"f8d704be-9a13-476f-81b5-35c35840c6c5","resolution":{"observed_at":"2026-08-11T14:07:16.290069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-10T21:42:11.638902Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.04302","last_updated":"2025-01-08T06:26:16Z","snapshot_observed_at":"2026-08-19T13:22:12.000698Z","submitted_at":"2025-01-08T06:26:16Z","title":"H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T21:42:11.638902Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2501.04302"},"observation_digest":"sha256:8de6e08a2d9f55b95286104e7c36e5a2e2e5fcaaba544222dd0559745c2cd52d","observation_id":"80310e18-8d5b-41e1-b8ac-3c864e810eb0","resolution":{"observed_at":"2026-08-10T21:42:11.638902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-10T20:38:56.427343Z","title":"Vivim: a video vision mamba for medical video object segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.07810","last_updated":"2025-01-14T03:20:20Z","snapshot_observed_at":"2026-08-19T09:32:36.692007Z","submitted_at":"2025-01-14T03:20:20Z","title":"AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:38:56.427343Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2501.07810"},"observation_digest":"sha256:63cc3a1f93c5d3dda4b879b690cf7e1aed943d25872bfe78c2e1b5f48c4f9565","observation_id":"221a2156-c4f3-40e4-a154-0fb0854e15fc","resolution":{"observed_at":"2026-08-10T20:38:56.427343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-09T18:58:26.032095Z","title":"Vivim: a video vision mamba for medical video object segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-14T11:13:02.177677Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.032095Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:ba0c72db46827661c7691ed5307e6a2e2cd758e40fb0e5a81994aad525ef377a","observation_id":"30d9f921-a7be-407d-9a62-dbbe1835b08a","resolution":{"observed_at":"2026-08-09T18:58:26.032095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-08T13:40:59.527417Z","title":"Vivim: a video vision mamba for medical video object segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07161","last_updated":"2025-02-11T00:59:30Z","snapshot_observed_at":"2026-08-15T17:07:34.565918Z","submitted_at":"2025-02-11T00:59:30Z","title":"A Survey on Mamba Architecture for Vision Applications","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:59.527417Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2502.07161"},"observation_digest":"sha256:646226fe290bc6c7608c274da3f3dfdfa74c4c40009d00796b1e9ad8def918db","observation_id":"47fba5fc-418b-4aa9-ad4e-4b70fa6bacaf","resolution":{"observed_at":"2026-08-08T13:40:59.527417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-16T11:55:02.023484Z","title":"Vivim: A video vision mamba for medical video segmentation.arXiv preprint arXiv:2401.14168, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14371","last_updated":"2025-06-26T03:50:04Z","snapshot_observed_at":"2026-08-18T15:00:26.687563Z","submitted_at":"2025-04-19T18:14:35Z","title":"Efficient Spiking Point Mamba for Point Cloud Analysis","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-16T11:55:02.023484Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2504.14371"},"observation_digest":"sha256:c7ac07026abd07c3ee2588d5344fefb740409699c7c02f7ee06367a79875cb1b","observation_id":"3fdd54e3-9836-4127-99b3-f649f8d2ac7b","resolution":{"observed_at":"2026-08-16T11:55:02.023484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-15T21:55:33.925438Z","title":"arXiv preprint arXiv:2401.14168 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08581","last_updated":"2025-05-13T13:56:10Z","snapshot_observed_at":"2026-08-19T17:54:17.852122Z","submitted_at":"2025-05-13T13:56:10Z","title":"ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:55:33.925438Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2505.08581"},"observation_digest":"sha256:f42c019a50bffd79ade941e6a152fa2607cff9ef293a1e98e3b5e2851ea94027","observation_id":"24a8f4df-54de-426b-9a35-b76909fcbe98","resolution":{"observed_at":"2026-08-15T21:55:33.925438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-07T10:52:20.644528Z","title":"arXiv preprint arXiv:2401.14168 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.04116","last_updated":"2025-06-09T01:39:48Z","snapshot_observed_at":"2026-08-14T02:13:13.664545Z","submitted_at":"2025-06-04T16:09:19Z","title":"A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:52:20.644528Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2506.04116"},"observation_digest":"sha256:f13ebf4bf572224cf91c823f56f6f4f7cb2a91858cd4df772d2b8bbfc6ce58aa","observation_id":"473af356-ae38-4141-8195-b8864947c756","resolution":{"observed_at":"2026-08-07T10:52:20.644528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-06T23:20:07.336877Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18679","last_updated":"2025-07-15T07:59:56Z","snapshot_observed_at":"2026-08-18T21:39:35.002880Z","submitted_at":"2025-06-23T14:22:49Z","title":"MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation","version":2},"reference_index":152,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:07.336877Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2506.18679"},"observation_digest":"sha256:d2d1fa322c9bd40d4c5524de2af62fae32b65bffa374c9a8d7127e73a5f3a771","observation_id":"0d61a7e8-b67f-47ed-8b33-5781f635ac1b","resolution":{"observed_at":"2026-08-06T23:20:07.336877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-06T19:01:20.540836Z","title":"Vivim: a video vision mamba for medical video object segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06689","last_updated":"2025-07-09T09:33:23Z","snapshot_observed_at":"2026-08-18T02:28:03.679130Z","submitted_at":"2025-07-09T09:33:23Z","title":"Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T19:01:20.540836Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2507.06689"},"observation_digest":"sha256:43a7e9543b4953116034ee8c53364697327f0cb879bfe9b52fc6f18a80a937a9","observation_id":"37b69cc7-9e53-422e-b699-4e8e2c500018","resolution":{"observed_at":"2026-08-06T19:01:20.540836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-06T16:16:49.475482Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14042","last_updated":"2025-07-18T16:11:28Z","snapshot_observed_at":"2026-08-21T06:14:31.874121Z","submitted_at":"2025-07-18T16:11:28Z","title":"Training-free Token Reduction for Vision Mamba","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T16:16:49.475482Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2507.14042"},"observation_digest":"sha256:a2e6ea13e7947ff204d9a18807e736cd4a030fcf0121bff80addeeaf68631f40","observation_id":"331d22b7-600d-48da-b2b3-c539ab573e1e","resolution":{"observed_at":"2026-08-06T16:16:49.475482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-06T15:20:11.176482Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16191","last_updated":"2025-08-20T02:43:14Z","snapshot_observed_at":"2026-08-19T21:15:34.964065Z","submitted_at":"2025-07-22T03:07:50Z","title":"Explicit Context Reasoning with Supervision for Visual Tracking","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T15:20:11.176482Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2507.16191"},"observation_digest":"sha256:506e8e9e04a70d833ad70e70ac8c1d33bc10db62316a190f5acea387f43b0a6f","observation_id":"79c2b040-56e9-4578-bd52-3eca28930e5f","resolution":{"observed_at":"2026-08-06T15:20:11.176482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-06T11:38:37.514784Z","title":"arXiv preprint arXiv:2401.14168 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22530","last_updated":"2025-07-31T03:01:47Z","snapshot_observed_at":"2026-08-11T21:40:20.267418Z","submitted_at":"2025-07-30T09:57:38Z","title":"HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T11:38:37.514784Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2507.22530"},"observation_digest":"sha256:3a202c66633350b308eef04dcdf561172b3b3d996ee28c847f46b8eff15ef5d9","observation_id":"1e6f18b5-e722-477b-aa5d-3a985f0a820b","resolution":{"observed_at":"2026-08-06T11:38:37.514784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-05T22:19:05.370579Z","title":"Vivim: a video vision mamba for medical video object segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.07237","last_updated":"2025-08-10T08:33:03Z","snapshot_observed_at":"2026-08-20T20:49:06.005389Z","submitted_at":"2025-08-10T08:33:03Z","title":"ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T22:19:05.370579Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2508.07237"},"observation_digest":"sha256:d2b5f8717855f7854e84122c41d2c6d046fecc494149d8ed1302a947ce24262e","observation_id":"6f9357ba-53db-4b73-a174-67da4ad89b42","resolution":{"observed_at":"2026-08-05T22:19:05.370579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-05T16:21:44.232294Z","title":"arXiv preprint arXiv:2401.14168 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.18681","last_updated":"2025-08-26T05:04:49Z","snapshot_observed_at":"2026-08-17T20:06:12.307711Z","submitted_at":"2025-08-26T05:04:49Z","title":"Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T16:21:44.232294Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2508.18681"},"observation_digest":"sha256:cbeb9010d492dfec8c6bbf16b0335d9dc84c9abe1c8ab9cb75eb1453a993c9c2","observation_id":"c0973d56-af2e-4537-892a-561ffb154495","resolution":{"observed_at":"2026-08-05T16:21:44.232294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-08-05T15:38:04.527673Z","title":"Vivim: A video vision mamba for medical video segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19705","last_updated":"2025-08-27T09:12:38Z","snapshot_observed_at":"2026-08-18T12:26:09.792526Z","submitted_at":"2025-08-27T09:12:38Z","title":"FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T15:38:04.527673Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2508.19705"},"observation_digest":"sha256:dc3846ca957304c6bc1080be3762f91e06ca6e3f81da822874ceaa9542494e25","observation_id":"56f41cfb-aa8f-4d7b-a4f4-0aec1062369d","resolution":{"observed_at":"2026-08-05T15:38:04.527673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":"2401.14168","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-04T09:49:44.637637Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":"0d4a532a-99aa-4b6a-b1a0-53c459b0999e","year":2024},"citing_paper":{"arxiv_id":"2604.26461","last_updated":"2026-04-29T09:17:03Z","snapshot_observed_at":"2026-08-15T04:21:53.221272Z","submitted_at":"2026-04-29T09:17:03Z","title":"$\\text{PKS}^4$:Parallel Kinematic Selective State Space Scanners for Efficient Video Understanding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-07T13:50:31.756981Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2604.26461"},"observation_digest":"sha256:99888d63d3f188a800f5e1583bb873aeb65309c4db3523b013b175de0aab8c33","observation_id":"f9a9494d-19c5-4d1f-a143-6e5960177f36","resolution":{"observed_at":"2026-05-12T08:46:25.963880Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":"2401.14168","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-04T09:49:44.637637Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":"0d4a532a-99aa-4b6a-b1a0-53c459b0999e","year":2024},"citing_paper":{"arxiv_id":"2606.10395","last_updated":"2026-06-09T04:16:39Z","snapshot_observed_at":"2026-08-13T13:04:35.298206Z","submitted_at":"2026-06-09T04:16:39Z","title":"Efficient RWKV-based Representation Learning for 3D Point Clouds","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-27T14:19:19.121022Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2606.10395"},"observation_digest":"sha256:0ff04edfbbc83b69192a5e8d907a3380309d0646bf8a6e415d5e3612a14c868e","observation_id":"2770c4e8-0a53-4a9b-8ef6-e65493fd92f7","resolution":{"observed_at":"2026-07-03T03:57:38.784337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":"2401.14168","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-04T09:49:44.637637Z","title":"Vivim: a video vision mamba for medical video object segmentation","venue":null,"work_id":"0d4a532a-99aa-4b6a-b1a0-53c459b0999e","year":2024},"citing_paper":{"arxiv_id":"2606.23126","last_updated":"2026-06-22T10:14:06Z","snapshot_observed_at":"2026-08-12T23:08:36.334748Z","submitted_at":"2026-06-22T10:14:06Z","title":"MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-06-26T09:26:51.456652Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2606.23126"},"observation_digest":"sha256:732562a3613c2fd655659dad85b899bc41628e8d7cd2736e6333037920f23c7f","observation_id":"b404e9bf-98e6-4264-8a8c-9bb2b1dcc8de","resolution":{"observed_at":"2026-07-04T09:49:44.639019Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14168","snapshot_observed_at":"2026-07-12T06:06:47.233814Z","title":"arXiv preprint arXiv:2401.14168 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.02922","last_updated":"2026-07-03T03:28:25Z","snapshot_observed_at":"2026-08-12T20:40:40.836906Z","submitted_at":"2026-07-03T03:28:25Z","title":"STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-07-12T06:06:47.233814Z"},"links":{"cited_paper":"/paper/2401.14168","citing_paper":"/paper/2607.02922"},"observation_digest":"sha256:c613721b90883dfbf8f2d279e464cee9eadb0cee2f9e1f36d37544f4bacb752b","observation_id":"7ed5a5f4-7b5f-4753-8be1-b4a8a65b32ec","resolution":{"observed_at":"2026-07-12T06:06:47.233814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.14168/citation-record","integrity":"/paper/2401.14168/integrity","json":"/paper/2401.14168/citation-record.json","paper":"/paper/2401.14168"},"outbound":[],"paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T14:24:34.030022Z","submitted_at":"2024-01-25T13:27:03Z","title":"Vivim: a Video Vision Mamba for Medical Video 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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2401.14168."}