{"as_of":"2026-08-05T22:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:51ac4eff0c409c426d225d32d2a1e56e946be61443b2c6951e650da7d7158ac4","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T21:12:07.539556Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T03:40:26.565029Z","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":"2026-07-03T17:48:46.259474Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"cited_work":{"arxiv_id":"2511.16618","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2511.16618","snapshot_observed_at":"2026-07-29T02:25:10.326992Z","title":"Sam2s: Segment anything in surgical videos via semantic long- term tracking.arXiv preprint arXiv:2511.16618, 2025","venue":null,"work_id":"8e6187fb-014b-4f62-9fce-6d99ab534ace","year":2025},"citing_paper":{"arxiv_id":"2606.17279","last_updated":"2026-06-15T20:40:45Z","snapshot_observed_at":"2026-08-02T14:53:37.247625Z","submitted_at":"2026-06-15T20:40:45Z","title":"Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T03:40:26.565029Z"},"links":{"cited_paper":"/paper/2511.16618","citing_paper":"/paper/2606.17279"},"observation_digest":"sha256:4f21a9c6efa51d0a8038ee6d5aa730c2fb61ca77e173e86e4cb9a5da2a0a6769","observation_id":"902c354f-823e-47d2-9653-11eb33a6a783","resolution":{"observed_at":"2026-07-29T02:25:10.326992Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2511.16618/citation-record","integrity":"/paper/2511.16618/integrity","json":"/paper/2511.16618/citation-record.json","paper":"/paper/2511.16618"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:02.857466Z","title":"Deep learning for surgical instrument recog- nition and segmentation in robotic-assisted surgeries: a sys- tematic review.Artificial Intelligence Review, 58(1):1, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:02.857466Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:c5ce5f0dedc90b966ff014a99083ec8c30212ecd92325bb5b1085c34a04dc5cb","observation_id":"f9e5af25-d0e8-41fa-b52b-f17266a48fbe","resolution":{"observed_at":"2026-08-03T21:12:02.857466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:02.935821Z","title":"Cholecinstanceseg: A tool instance segmentation dataset for laparoscopic surgery.Scientific Data, 12(1):825, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:02.935821Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:76680ca4399aab6d73b06f21ae5ac134956179cacaafc955341da0e77e8b1b5e","observation_id":"f8f2e0c1-243e-4836-957d-87b70b40bfb5","resolution":{"observed_at":"2026-08-03T21:12:02.935821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.13815","last_updated":"2021-12-27T18:06:12Z","snapshot_observed_at":"2026-07-06T12:22:46.784870Z","submitted_at":"2021-12-27T18:06:12Z","title":"Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.13815","snapshot_observed_at":"2026-08-03T21:12:03.024171Z","title":"Temporally constrained neural networks (tcnn): A framework for semi-supervised video semantic segmentation.arXiv preprint arXiv:2112.13815, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.024171Z"},"links":{"cited_paper":"/paper/2112.13815","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:26c06036af2ac3d50a2346af4c5d8283299f92489f09835189d8c119c34b67d9","observation_id":"74509df7-e127-46c1-83e0-d7921bde478c","resolution":{"observed_at":"2026-08-03T21:12:03.024171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.087470Z","title":"A multi-centre polyp detection and segmentation dataset for generalisability assessment.Scientific Data, 10(1):75, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.087470Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:a6eb23a8bf3412c75aa9eab80b7a1b17e023760a83b06735751efa3e4f3c9f7c","observation_id":"3c0e700d-83ad-4b8c-8c51-7afdc4323fea","resolution":{"observed_at":"2026-08-03T21:12:03.087470Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.06426","last_updated":"2019-02-21T17:01:02Z","snapshot_observed_at":"2026-07-06T07:33:44.696417Z","submitted_at":"2019-02-18T07:08:36Z","title":"2017 Robotic Instrument Segmentation Challenge","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.06426","snapshot_observed_at":"2026-08-03T21:12:03.135925Z","title":"2017 robotic instrument segmentation challenge.arXiv preprint arXiv:1902.06426, 2019","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.135925Z"},"links":{"cited_paper":"/paper/1902.06426","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:1b7099b9e0515e7782a37badb8f441be3b1c8e0a63c2b02e2c61696c37b7a121","observation_id":"cd41ac32-1df3-4e41-af6d-4d5d755562fb","resolution":{"observed_at":"2026-08-03T21:12:03.135925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.11190","last_updated":"2020-08-03T01:55:24Z","snapshot_observed_at":"2026-08-05T21:16:20.262505Z","submitted_at":"2020-01-30T06:37:07Z","title":"2018 Robotic Scene Segmentation Challenge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.11190","snapshot_observed_at":"2026-08-03T21:12:03.258472Z","title":"2018 robotic scene segmentation challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.258472Z"},"links":{"cited_paper":"/paper/2001.11190","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:f37ef86cbc9729532110224ec08f4aab9e099326f8b467227c16e2109cd24748","observation_id":"1b713460-6f3d-488c-9995-0b5a51ca235a","resolution":{"observed_at":"2026-08-03T21:12:03.258472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.387757Z","title":"Matis: Masked-attention transformers for surgical instrument segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.387757Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:5e3ad4f2d950b536b8f2182478473935eccae10455535bfbb0e5c70ee3e8bcf7","observation_id":"b2bb9bc7-4309-40e5-a53d-3d716f92ae7e","resolution":{"observed_at":"2026-08-03T21:12:03.387757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.546631Z","title":"Pixel-wise recognition for holistic surgical scene understanding.arXiv, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.546631Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:4534b1c2b4192bc7f093335425a3be5b5dcfdaa95f5b19fa0ef73f7ff4e6057c","observation_id":"1847944e-7dc9-4d96-9ad3-0b5495a6501b","resolution":{"observed_at":"2026-08-03T21:12:03.546631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.678232Z","title":"Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.678232Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:b563a2abf34dd83e20a8b5a09abac570944084df9c9ce68460f791f38f1d355c","observation_id":"bb11c067-8efe-44b9-9b80-a49ff863809c","resolution":{"observed_at":"2026-08-03T21:12:03.678232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.787652Z","title":"The dresden surgical anatomy dataset for abdominal organ segmentation in surgi- cal data science.Scientific Data, 10(1):1–8, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.787652Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:91fe8de5063c108cd802a4636199a25f62e5d5e5aa181de15bdf781334694217","observation_id":"00208e69-7843-4d05-aa50-a0cd51a7628d","resolution":{"observed_at":"2026-08-03T21:12:03.787652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.847941Z","title":"Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.847941Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:c057405605c624bb3597ea1606d9d18562c0a6a9a129473e624d2d99b89e3c37","observation_id":"3d95805f-3d08-4d3f-875e-c6bf15af9706","resolution":{"observed_at":"2026-08-03T21:12:03.847941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.910811Z","title":"Putting the object back into video object segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.910811Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:82ffd818438b745bacb8d306c9076041f6649597c4f0f6943728539fe64393ab","observation_id":"fbf5854a-889a-4906-8088-e9c8f274d106","resolution":{"observed_at":"2026-08-03T21:12:03.910811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:03.952399Z","title":"Mosev2: A more challenging dataset for video object segmentation in complex scenes.arXiv preprint arXiv:2508.05630, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:03.952399Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:99a10c8db66e9bdcc9faab4809f1b6abc14759d06e516204fc7f37b28438c64d","observation_id":"727689ab-d726-43c1-ab63-fcf88767dda2","resolution":{"observed_at":"2026-08-03T21:12:03.952399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.012442Z","title":"Sam2long: Enhancing sam 2 for long video segmentation with a training-free memory tree","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.012442Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:a7d67e2590a1523dee5a21430e7a46527ae613e0ab353decef8d07b6c66fac27","observation_id":"40ed8da1-a8d5-44fa-95b3-946c4684ff65","resolution":{"observed_at":"2026-08-03T21:12:04.012442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.070011Z","title":"Patch-based adaptive weighting with segmenta- tion and scale (pawss) for visual tracking in surgical video","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.070011Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:a0baf3d3988e7d1b5191eb9d1a8032e19ce6f0da51c447cd7ecfd70f651602ea","observation_id":"6e330f1d-be37-451d-b6ad-cfc6be4813df","resolution":{"observed_at":"2026-08-03T21:12:04.070011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.141724Z","title":"Spatio-temporal represen- tation decoupling and enhancement for federated instru- ment segmentation in surgical videos.arXiv preprint arXiv:2506.23759, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.141724Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:7c6de274bd79bbe6a44ef8c2877af920d7907283d5c168a77f659fef56f57a13","observation_id":"8f6aa669-3deb-4a98-89a5-dd4d204197ab","resolution":{"observed_at":"2026-08-03T21:12:04.141724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.201914Z","title":"Deep learning for video object segmentation: a review.Artificial Intelligence Review, 56(1):457–531, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.201914Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:39d821f363d5c4ff0b657c59b67245e4d41b69b839d69a30a5484ef42d62b94e","observation_id":"a37cdca1-9ad8-4c6e-a7a2-77cf2cfabbef","resolution":{"observed_at":"2026-08-03T21:12:04.201914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.288806Z","title":"Image compositing for segmentation of surgical tools without manual annotations.IEEE transactions on medical imaging, 40(5):1450–1460, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.288806Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:9170c80923ad990fc050f85888d5def52b8a561270643a27618e550106b83861","observation_id":"3f8a0a57-de02-45ff-9afb-fd2ab25754a5","resolution":{"observed_at":"2026-08-03T21:12:04.288806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.370697Z","title":"Softseg: Advantages of soft versus binary training for image segmentation.Medical image analysis, 71:102038, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.370697Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:502e6719c39ce1edf142bb3356a3122ff707484598b0a2677c6c8b26cbd6975e","observation_id":"7bd23acf-0db5-4915-b5c8-68e8ae1faf0e","resolution":{"observed_at":"2026-08-03T21:12:04.370697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.414645Z","title":"Detection, segmentation, and 3d pose es- timation of surgical tools using convolutional neural net- works and algebraic geometry.Medical Image Analysis, 70: 101994, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.414645Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:157544ce1c8a10e2cdf24df4a3a6ddd114688f05be34390abf10db7d226cdbf1","observation_id":"504f777b-7417-498e-a223-b0da66e3f35d","resolution":{"observed_at":"2026-08-03T21:12:04.414645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.488077Z","title":"Interac- tive video object segmentation using global and local trans- fer modules","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.488077Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:0a58bd40cdd9b7dc2b46af823cd875a4788328b57e11f7100d351932f2f8e186","observation_id":"22b9d0c8-2e4e-4af2-8134-110d42c5bcad","resolution":{"observed_at":"2026-08-03T21:12:04.488077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.573439Z","title":"Lvos: A benchmark for long-term video object segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.573439Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:dc72e7db78ff361f94096c447c01d393e17aa35c1939ade501fb6501c7996e68","observation_id":"3a05c61a-bc18-4ab0-a56e-ab8844313cbd","resolution":{"observed_at":"2026-08-03T21:12:04.573439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.657152Z","title":"Lvos: A benchmark for large- scale long-term video object segmentation.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.657152Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:219fa3c2edf48d4f5e4cd7977ea06cfbc90f57797515b251f54273ea6a0b30a5","observation_id":"5c24f91d-77b2-4514-83ac-2b4c290eefe4","resolution":{"observed_at":"2026-08-03T21:12:04.657152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.12453","last_updated":"2020-12-23T02:23:15Z","snapshot_observed_at":"2026-07-06T10:27:10.375609Z","submitted_at":"2020-12-23T02:23:15Z","title":"CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.12453","snapshot_observed_at":"2026-08-03T21:12:04.739163Z","title":"Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80.arXiv preprint arXiv:2012.12453, 2020","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.739163Z"},"links":{"cited_paper":"/paper/2012.12453","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:f4e1f3b958933a6a97725b704efa00db6784cf7c286400abf4054279d5a9325c","observation_id":"1a820113-e8ee-45a1-b4ab-443be46990d3","resolution":{"observed_at":"2026-08-03T21:12:04.739163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.818831Z","title":"Domain and content adaptive convolution based multi- source domain generalization for medical image segmenta- tion.IEEE Transactions on Medical Imaging, 42(1):233– 244, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.818831Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:f6fc72bb2a7b683cfc4808a3ec203476c947891cdce97ace8f840c8c87dc3c22","observation_id":"105801d1-081b-4045-a115-6ccf08f14e4a","resolution":{"observed_at":"2026-08-03T21:12:04.818831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.870355Z","title":"Kvasir-seg: A segmented polyp dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.870355Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:5667d0da082cdaac70bc27418f3e1a589122a050545877a3ae2b3d396823e3aa","observation_id":"f38708f9-c6fe-4e69-aa47-a196dc159168","resolution":{"observed_at":"2026-08-03T21:12:04.870355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:04.954607Z","title":"Exploring intra-and inter-video relation for surgical semantic scene segmentation.IEEE Transactions on Medical Imaging, 41(11):2991–3002, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:04.954607Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:620f8cfa0982e86fcfe29e8102d39c785b11f03472bc9cba2ad4151960706930","observation_id":"337687a8-e161-417e-8a5c-73da2ad6684d","resolution":{"observed_at":"2026-08-03T21:12:04.954607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.015315Z","title":"Segment any- thing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.015315Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:75748d2ebf8f46380a3f8cf05337d3f3b39277aeac6ea1220c077113fb18e031","observation_id":"a3074429-dafb-4c91-b399-07b0c6193e1f","resolution":{"observed_at":"2026-08-03T21:12:05.015315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.127043Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.127043Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:a1d54ce909675fe01c455a7bb6e351c29fad3d7e5277947c923dff8238d1626d","observation_id":"b83f2659-3beb-4dd7-bbce-985e76426350","resolution":{"observed_at":"2026-08-03T21:12:05.127043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.202395Z","title":"Open-vocabulary semantic segmentation with mask-adapted clip","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.202395Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:c3edc652adc48b2d4d46dc0129f1f82ad76bf04db826014cdd8b63e671e7b641","observation_id":"a2143051-8be0-4244-8c12-f02ef0c5484a","resolution":{"observed_at":"2026-08-03T21:12:05.202395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.258193Z","title":"Video object segmentation with adaptive feature bank and uncertain-region refinement.Advances in Neural Informa- tion Processing Systems, 33:3430–3441, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.258193Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:eaaa6ca0ff43b11290b0737750460899e58b3bca415f12559400b411a2374472","observation_id":"5bee79b3-b99c-4392-a10b-391692ebe92d","resolution":{"observed_at":"2026-08-03T21:12:05.258193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.343825Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.343825Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:74d9e660367afe8838a6d0aec669f183b0b1a5289f4be34d8d8034181f9a9c44","observation_id":"07fe755e-e08b-460a-91d4-ee4a87d7108c","resolution":{"observed_at":"2026-08-03T21:12:05.343825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.425484Z","title":"Surgical sam 2: Real-time segment anything in surgical video by efficient frame pruning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.425484Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:fe6c21039f32424d1ab87f347f6fcf1a377316356b97407c3ccf6973dfa95a39","observation_id":"7669acdc-8e09-45a4-b5ab-c2e860b2463a","resolution":{"observed_at":"2026-08-03T21:12:05.425484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.08581","last_updated":"2025-05-13T13:56:10Z","snapshot_observed_at":"2026-07-06T21:23:14.666121Z","submitted_at":"2025-05-13T13:56:10Z","title":"ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.08581","snapshot_observed_at":"2026-08-03T21:12:05.507774Z","title":"Resurgsam2: Referring segment anything in surgical video via credible long-term tracking.arXiv preprint arXiv:2505.08581, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.507774Z"},"links":{"cited_paper":"/paper/2505.08581","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:5eaea985f64648467b917fac268f4de4bd8689d41341d31770677e78c013e672","observation_id":"9121928a-c066-43f9-8931-f02add9b1640","resolution":{"observed_at":"2026-08-03T21:12:05.507774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.580364Z","title":"Learning high-quality dynamic memory for video object segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.580364Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:08d739fadd627ae5b3d46178776fcc9b86035648f965fbd0f009220c0d708b08","observation_id":"a593f9d3-ea9b-49e0-b295-51b6589df999","resolution":{"observed_at":"2026-08-03T21:12:05.580364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12429","last_updated":"2024-10-25T17:28:43Z","snapshot_observed_at":"2026-07-06T17:05:31.586924Z","submitted_at":"2023-12-19T18:56:44Z","title":"The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.12429","snapshot_observed_at":"2026-08-03T21:12:05.663088Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.663088Z"},"links":{"cited_paper":"/paper/2312.12429","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:bd21f4403d9cee83fd2a0b22aafe0ccef42a45e9bd9b21e3b60dcd29489ef344","observation_id":"1d3cf7cc-2a6d-4716-99d6-6d0d7c8131cf","resolution":{"observed_at":"2026-08-03T21:12:05.663088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.743286Z","title":"Pyramid attention aggregation network for semantic segmentation of surgical instruments","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.743286Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:65aa695604a4dc844eae57baa24fdf7115e6747d9c2ef2ae91210268f9769ea6","observation_id":"59888796-a2f5-4906-8c78-d347431207d3","resolution":{"observed_at":"2026-08-03T21:12:05.743286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.837827Z","title":"Surginet: Pyramid attention aggregation and class-wise self- distillation for surgical instrument segmentation.Medical Image Analysis, 76:102310, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.837827Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:1fc61c920eda83b4997815c77ac6a4bb0898e5dc7a8f08993041e69a20efb930","observation_id":"c688eb31-da70-471b-9046-ac9eb3d6cd63","resolution":{"observed_at":"2026-08-03T21:12:05.837827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.901934Z","title":"Video object segmentation using space-time memory networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.901934Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:a2c038fff6d858e925e5d775e22b0bd281816c72d42e01425c83bfd9ad783511","observation_id":"e28a8af9-2099-41f4-b01a-f94ff16eef3d","resolution":{"observed_at":"2026-08-03T21:12:05.901934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:05.947673Z","title":"Mvd-net: Semantic segmentation of cataract surgery using multi-view learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:05.947673Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:0dc57d55b0088d286a68f037a228b1b851ff10d25d8627e6c8ccfbca26f96b8c","observation_id":"eb09073c-733a-4f40-b7f7-e4a0f3ba02b3","resolution":{"observed_at":"2026-08-03T21:12:05.947673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00496","last_updated":"2024-01-23T23:30:57Z","snapshot_observed_at":"2026-08-02T18:08:38.646548Z","submitted_at":"2023-12-31T13:32:18Z","title":"SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00496","snapshot_observed_at":"2026-08-03T21:12:06.012090Z","title":"Sar-rarp50: Segmentation of surgical instrumentation and action recognition on robot-assisted radical prostatectomy challenge.arXiv preprint arXiv:2401.00496, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.012090Z"},"links":{"cited_paper":"/paper/2401.00496","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:056a5602d066d66907fa8ccf916f836ccc4dbf04c3bfc22c357570f3405f4400","observation_id":"bbc16f62-d5ab-4b3c-ad61-6af75f7cc695","resolution":{"observed_at":"2026-08-03T21:12:06.012090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.066615Z","title":"Structure matters: Revisiting boundary refinement in video object seg- mentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.066615Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:ac60cc189496cd18be081ea13602be68d99e857ff7238034d2f01ae7968a1d0d","observation_id":"7a6cacfb-6bfd-41b4-8f10-8bd1d86c27b9","resolution":{"observed_at":"2026-08-03T21:12:06.066615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.147869Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.147869Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:45471d51ede12776ccbabb2a116190e2fc576bca162e3d450c5b565823a555a5","observation_id":"4c8def3f-de2b-40e6-b3c8-12c4c96f3789","resolution":{"observed_at":"2026-08-03T21:12:06.147869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-03T21:12:06.211859Z","title":"Sam 2: Segment anything in images and videos.arXiv preprint arXiv:2408.00714, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.211859Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:76f05805d34d6a8c6c510745da2d773a72eab939332f3c0f0b39c704637ae6f6","observation_id":"e2be5e63-9615-4e78-8a23-7d33ef297f87","resolution":{"observed_at":"2026-08-03T21:12:06.211859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.282241Z","title":"Towards real-time multiple surgical tool tracking.Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9(3): 279–285, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.282241Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:2d80c6fe085bc88859a727a7eb4b347f7233b6e248a931b10ffcdc869d181992","observation_id":"956af7f3-636a-46c6-b0fb-9fda72b282eb","resolution":{"observed_at":"2026-08-03T21:12:06.282241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.386636Z","title":"FA Davis, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.386636Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:3ef3b86e7bced32b939b787c805d7bf7138e6303d5aaf705130991ed6d181b33","observation_id":"1831f7ef-4053-4449-aaaf-a2a76a70d8a2","resolution":{"observed_at":"2026-08-03T21:12:06.386636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.559018Z","title":"Towards holistic surgical scene understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.559018Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:495effdaaf0c190bcd8063b86971c396336e19fe5c8f02ed618bb7385d941ff2","observation_id":"75ab87ee-b5e6-467e-b90e-c27b3232368a","resolution":{"observed_at":"2026-08-03T21:12:06.559018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.667391Z","title":"A distractor-aware memory for visual object tracking with sam2","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.667391Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:c69ef2ccc7d791a1e34c71d43368bd378f4a357b7348db05974bea6693c7ab00","observation_id":"f5052a61-e353-4e7f-b49b-9838ec1ef2a4","resolution":{"observed_at":"2026-08-03T21:12:06.667391Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.753317Z","title":"Autolaparo: A new dataset of integrated multi-tasks for image-guided surgical automation in laparoscopic hysterectomy","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.753317Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:aa836b41d2baf0f17664b85f5bce6476143bd2ab6462460b8b6f1d6a6d255215","observation_id":"48ec7216-17db-4641-bdc0-e66c91c3d6b6","resolution":{"observed_at":"2026-08-03T21:12:06.753317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:06.914161Z","title":"Scribbleprompt: fast and flexible interactive segmen- tation for any biomedical image","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:06.914161Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:d0e88cfb9d77830ea1dea0ddc62617f57c1d125f2a89adfe18a4b09d53656861","observation_id":"100ee813-66d0-499d-bf59-2323d51fd4c4","resolution":{"observed_at":"2026-08-03T21:12:06.914161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.11922","last_updated":"2024-11-30T22:32:34Z","snapshot_observed_at":"2026-07-06T19:52:11.774784Z","submitted_at":"2024-11-18T05:59:03Z","title":"SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.11922","snapshot_observed_at":"2026-08-03T21:12:07.077803Z","title":"Samurai: Adapting segment anything model for zero-shot visual tracking with motion-aware memory.arXiv preprint arXiv:2411.11922,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:07.077803Z"},"links":{"cited_paper":"/paper/2411.11922","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:f1b72d5aa5273ded25f8072d7f02ea8474f9d8e4fa06c269c402a0e9e51827b8","observation_id":"9e220242-c34f-4058-bf0a-10d6acb5ee61","resolution":{"observed_at":"2026-08-03T21:12:07.077803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:07.168173Z","title":"Decoupling features in hierar- chical propagation for video object segmentation.Advances in Neural Information Processing Systems, 35:36324–36336,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:07.168173Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:056cd801f9801d0ecc059d3f4bb0f5285d5998c15e0a66ef077f2cf8fd2282cf","observation_id":"3a0f717b-0602-4cfd-996a-214190fb733d","resolution":{"observed_at":"2026-08-03T21:12:07.168173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:07.292236Z","title":"Surgicalsam: Efficient class prompt- able surgical instrument segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:07.292236Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:a11ee81c9100711e421fd51d31df89baccb30b2cec5619261d598f44858d9097","observation_id":"a8856b6f-4ad7-4c9e-a0cb-507dae8478ac","resolution":{"observed_at":"2026-08-03T21:12:07.292236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T21:12:07.455629Z","title":"Sur- gai3","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:07.455629Z"},"links":{"citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:57216895bc0c8b910f39060846c70d25df0e2fa9279c86e8ac0d1728a864e179","observation_id":"bf38a46c-4f22-4ac5-9915-2e6b1a0d8a88","resolution":{"observed_at":"2026-08-03T21:12:07.455629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00874","last_updated":"2024-12-04T23:51:25Z","snapshot_observed_at":"2026-08-04T09:38:10.661882Z","submitted_at":"2024-08-01T18:49:45Z","title":"Medical SAM 2: Segment medical images as video via Segment Anything Model 2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00874","snapshot_observed_at":"2026-08-03T21:12:07.539556Z","title":"Medical sam 2: Seg- ment medical images as video via segment anything model 2.arXiv preprint arXiv:2408.00874, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T21:12:07.539556Z"},"links":{"cited_paper":"/paper/2408.00874","citing_paper":"/paper/2511.16618"},"observation_digest":"sha256:42c49ec11ffffa588903c9d2b01279f414c72928b0f4cf6570a99cae1e53ae5d","observation_id":"9e7a47d7-c2e7-4e64-92fc-b8d33693db6c","resolution":{"observed_at":"2026-08-03T21:12:07.539556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.16618","last_updated":"2026-07-28T09:58:43Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T01:06:41.070316Z","submitted_at":"2025-11-20T18:18:49Z","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":55,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":55},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2511.16618."}