{"as_of":"2026-08-10T23:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:96c25098ea63228d3ecd7b160674059d485b6edafbab5ef7cd64013342256633","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T18:58:26.314404Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.00462/citation-record","integrity":"/paper/2502.00462/integrity","json":"/paper/2502.00462/citation-record.json","paper":"/paper/2502.00462"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.723345Z","title":"From coarse to fine: Robust hierarchical localization at large scale,","venue":null,"work_id":"7f05f9c1-8670-4446-bda2-3bb20e7be330","year":2019},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.628145Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:78e742ea0422908dc7c883086e2fc3d7ec497257f845241a55b761b7b3f0b898","observation_id":"9b026d00-7c52-4f66-a65b-8cafc0216437","resolution":{"observed_at":"2026-08-09T18:58:27.727167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.711398Z","title":"Back to the feature: Learning robust camera localization from pixels to pose,","venue":null,"work_id":"25a0436a-11e3-40b9-9a26-3504b035bb1d","year":2021},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.632729Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:87ab046f2c8bd2870caf88f55eb53117448165722ccca66647382182c6df7696","observation_id":"2750e9cc-14fa-4169-9f5a-08ae3e0d0fc0","resolution":{"observed_at":"2026-08-09T18:58:27.715807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:25.636282Z","title":"VINS-Mono: A robust and versatile monocular visual-inertial state estimator,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.636282Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:068d9dd86bb04c5a9c6a521002b531e59a0a60bc96e93103f900462b288d2d42","observation_id":"e52f0048-56eb-40c1-b872-249448ae0b9c","resolution":{"observed_at":"2026-08-09T18:58:25.636282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.693720Z","title":"ORB-SLAM: A versatile and accurate monocular SLAM system,","venue":null,"work_id":"b2dc4d1c-9954-4a09-9a48-2ac0cf26517a","year":2015},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.639701Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:b64ec256f1b880a23a56762d8bf7d246949fd016da71da97e0bcf3953e3417ea","observation_id":"0f00a36a-c451-4857-8133-3f7684e8007f","resolution":{"observed_at":"2026-08-09T18:58:27.697696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.683082Z","title":"UV-SLAM: Unconstrained line-based SLAM using vanishing points for structural mapping,","venue":null,"work_id":"b051c8be-b728-4a3e-8ce2-e37b51f08c02","year":2022},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.643783Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:d74f99afafeb9198d83f3661f833f8e21588f81276950ab214d44d88ed028036","observation_id":"b5a0b113-a0cb-4c4a-a4e3-ca7587d5a46f","resolution":{"observed_at":"2026-08-09T18:58:27.686921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.570224Z","title":"Building rome in a day,","venue":null,"work_id":"5c232f96-af75-472d-aa56-e5f4c6751233","year":2011},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.647103Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:a2848ac26985ffc7a61b9acd1164faf91b851169972c1649feb282c80897f8a8","observation_id":"5590acfb-d369-4275-92b6-d82074672219","resolution":{"observed_at":"2026-08-09T18:58:27.636180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.531712Z","title":"Structure-from-Motion revisited,","venue":null,"work_id":"f3dde3d3-97a4-4a9f-895b-ae80ad6e6d5d","year":2016},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.651325Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:78f5dff06e7e8532f6ec659dab94dceb0f8564d3f8b5970006aee25c17c7341d","observation_id":"bc2d0dd0-64f1-492f-be5d-4ac3af2b8ce3","resolution":{"observed_at":"2026-08-09T18:58:27.550286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.464054Z","title":"R2D2: Reliable and repeatable detector and descriptor,","venue":null,"work_id":"7a1032de-bc01-46f3-bff1-12f30ef72007","year":2019},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.655429Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:ee0309069fca477f5f18ab739ca96edb3946da44d0426bcd4e2ba9c32c51c34e","observation_id":"d20e2d68-ee12-474c-8a2f-309355a78b66","resolution":{"observed_at":"2026-08-09T18:58:27.492739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.417442Z","title":"A local image descriptor robust to illumination changes,","venue":null,"work_id":"839ddf78-bd10-4e52-a53f-103ea4e4d456","year":2013},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.658573Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:655de159ca13dc6bb6bf0b7706c3bda5b6f2e6d58d2deab2cdeb4da3545f1fcc","observation_id":"16f57dc1-8a61-4804-9aa8-26c711344d9d","resolution":{"observed_at":"2026-08-09T18:58:27.421093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.406624Z","title":"LightGlue: Local feature matching at light speed,","venue":null,"work_id":"a2fff2cc-213b-49ce-95c9-4814b6a41b73","year":2023},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.662279Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:7bb64e7b6d93c6b512069ddf81c0f3361eb7205ce7146382ba4ca94cecb5d439","observation_id":"50836887-8d6d-45c8-a385-00dd6df88710","resolution":{"observed_at":"2026-08-09T18:58:27.410419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.395575Z","title":"SuperPoint: Self-supervised interest point detection and description,","venue":null,"work_id":"930a2b98-bdde-4dde-ba49-a1bfc2d9244a","year":2018},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.665868Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:2c83e1d2d043cd5beac03b23980f9a168f17b4dcf12cf82d50db8b6c72adb56e","observation_id":"b470c4af-f86e-48ae-9891-7317d9864f71","resolution":{"observed_at":"2026-08-09T18:58:27.399774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.384067Z","title":"Attention is all you need,","venue":null,"work_id":"c987692e-ca2b-43a0-a091-2c973d0c915d","year":2017},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.669937Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:e0f252edc9ba912a90686e0498556f54efd441390d227d8cdcf8c422cd2dbac6","observation_id":"7c4b283f-52d9-4b6d-8860-9e00ed91554f","resolution":{"observed_at":"2026-08-09T18:58:27.388074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.373455Z","title":"Emerging properties in self-supervised vision trans- formers,","venue":null,"work_id":"ec0bbbc2-1b3c-4f4e-96bc-b94bf5899b43","year":2021},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.674037Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:055f8adbfe7056aa35cd4d49d3597c05c950d965e5a67710839135039551367b","observation_id":"f3cbe18b-c8b7-4389-b999-d14444353f51","resolution":{"observed_at":"2026-08-09T18:58:27.377163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-09T18:58:25.677881Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.677881Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:cc8b182f37b65f32de770a0cf00056f279a82aa08fd57e60ed581b4c325f87a5","observation_id":"8e3f57de-22e5-4962-bca6-23efdc7217d6","resolution":{"observed_at":"2026-08-09T18:58:25.677881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.14795","last_updated":"2022-03-15T22:37:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-07-30T17:53:34Z","title":"Perceiver IO: A General Architecture for Structured Inputs & Outputs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.14795","snapshot_observed_at":"2026-08-09T18:58:25.681918Z","title":"Perceiver IO: A general architecture for structured inputs & outputs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.681918Z"},"links":{"cited_paper":"/paper/2107.14795","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:ce2d4051dba368a4af72e85792d4d86ea77d17b1ada25c62da7969b4847e9556","observation_id":"86933876-8b12-4b28-bd0e-79f1d7f1c8b5","resolution":{"observed_at":"2026-08-09T18:58:25.681918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.362842Z","title":"LoFTR: Detector- free local feature matching with transformers,","venue":null,"work_id":"4a6fc304-745d-48f3-bfcb-bc605785d5cb","year":2021},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.685846Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:d34eb6b6b7380df19757ebfde276bd41f3fd0963444bc17db936b0aab6e1cab8","observation_id":"13b00123-65e2-4c5f-9ef9-5253d31bd4c8","resolution":{"observed_at":"2026-08-09T18:58:27.365907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.352655Z","title":"Efficient neighbourhood consensus networks via submanifold sparse convolutions,","venue":null,"work_id":"03884533-053b-49e5-b4bc-d09e03b15e5c","year":2020},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.689407Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:4ca28de3a0fc66e34cc7b264b78fc21f20883f94ae8c1401f73743f325fe30bc","observation_id":"be0681eb-f3bd-4a52-9edc-867d81d1ea8b","resolution":{"observed_at":"2026-08-09T18:58:27.355947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.342170Z","title":"Dual-resolution correspon- dence networks,","venue":null,"work_id":"26331b8a-7088-478a-86e0-9410202bdcf3","year":2020},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.692648Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:eac3683d0b025cdc1edfe640c54589f322a9204b2f653c22fea30b1f7da4426e","observation_id":"de7c0905-a79d-47c7-b39b-843e5edd6b45","resolution":{"observed_at":"2026-08-09T18:58:27.345980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.331736Z","title":"Super- Glue: Learning feature matching with graph neural networks,","venue":null,"work_id":"f3f8b558-bbb3-45b3-b5c6-e01b241172cb","year":2020},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.696211Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:e37285a4fdd86b4b3f345aa388ea57fe979ada78fe62533b741ec6ea64c812a3","observation_id":"0f2105e1-964e-42a2-ba28-0a45d61c127e","resolution":{"observed_at":"2026-08-09T18:58:27.335129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.321060Z","title":"LaMAR: Benchmarking localization and mapping for augmented reality,","venue":null,"work_id":"a0d72eaf-e4c1-4d4d-bc37-e84ecda4c23b","year":2022},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.699676Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:375b7c1b0eb53e0ec68c431e870420ce1f92076738a408590be803a8f4e82d1f","observation_id":"21ee7daa-cbd1-4e62-9932-c197311d204c","resolution":{"observed_at":"2026-08-09T18:58:27.324780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.274541Z","title":"Bench- marking 6DOF outdoor visual localization in changing conditions,","venue":null,"work_id":"12ed6649-8c08-42ee-9926-6d27b643232c","year":2018},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.703088Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:fc1fa997cc366697fc6a30baf32e2607dfd62948ee7cfc703125e543049341f6","observation_id":"47f650f2-586f-452e-b5de-bec276e1af73","resolution":{"observed_at":"2026-08-09T18:58:27.314410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-09T18:58:25.719617Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.719617Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:c8cd1852c987345bac15d64546f8c38e042363cb5251708dc2db7edbc8e39766","observation_id":"e6945ed6-9d76-4fad-aa7d-d48eaa15153c","resolution":{"observed_at":"2026-08-09T18:58:25.719617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19887","last_updated":"2024-07-03T14:30:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-28T23:55:06Z","title":"Jamba: A Hybrid Transformer-Mamba Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.19887","snapshot_observed_at":"2026-08-09T18:58:25.754735Z","title":"Jamba: A hybrid transformer-mamba language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.754735Z"},"links":{"cited_paper":"/paper/2403.19887","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:2289e75ba93a2eeac372662e34569a0eb6f31ce148df26ec6c6f9f243774499c","observation_id":"4b683571-8ea5-4544-b32f-fc25a6ed97f8","resolution":{"observed_at":"2026-08-09T18:58:25.754735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08083","last_updated":"2025-03-25T17:54:37Z","snapshot_observed_at":"2026-08-06T23:38:21.068023Z","submitted_at":"2024-07-10T23:02:45Z","title":"MambaVision: A Hybrid Mamba-Transformer Vision Backbone","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.08083","snapshot_observed_at":"2026-08-09T18:58:25.787311Z","title":"MambaVision: A hybrid mamba- transformer vision backbone,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.787311Z"},"links":{"cited_paper":"/paper/2407.08083","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:5460619f30bbdd29eda5ce9693766b6a26c2af1bb3f9e669c84bb70810a0c29e","observation_id":"c272c1cd-5569-4bf5-b068-326d90336a32","resolution":{"observed_at":"2026-08-09T18:58:25.787311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-07-06T17:16:59.193820Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-09T18:58:25.843116Z","title":"Vision Mamba: Efficient visual representation learning with bidirectional state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.843116Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:74dc6877dafc37a628f7b8d4367e3a16f2ef05d409bbd6c9f1bd26277f2d215f","observation_id":"3ff4b6d3-1e94-4323-a857-2facd6de6d2f","resolution":{"observed_at":"2026-08-09T18:58:25.843116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14174","last_updated":"2024-05-23T04:59:49Z","snapshot_observed_at":"2026-08-09T04:01:06.867296Z","submitted_at":"2024-05-23T04:59:49Z","title":"Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14174","snapshot_observed_at":"2026-08-09T18:58:25.879812Z","title":"Multi-scale VMamba: Hierarchy in hierarchy visual state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.879812Z"},"links":{"cited_paper":"/paper/2405.14174","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:09cd3c244f2ec84a20c5fd944347d06595c7c02577cff932f91a1ab5e79e8753","observation_id":"9bf93b19-6d46-46ee-b335-0112d217d229","resolution":{"observed_at":"2026-08-09T18:58:25.879812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.164788Z","title":"Object retrieval with large vocabularies and fast spatial matching,","venue":null,"work_id":"c09d299f-08b1-4ba2-b587-f5e2953157d2","year":2007},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.930890Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:552cc6534c3c4caa70708ccc60db2ddd1f7ed79c56478ca0e43858bb04bf9ba1","observation_id":"85ee5466-a719-43ee-bc81-426b67938332","resolution":{"observed_at":"2026-08-09T18:58:27.192217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.077451Z","title":"Video Google: A text retrieval approach to object matching in videos,","venue":null,"work_id":"b2956e37-ffc5-40d4-8d9d-703f822c1353","year":2003},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.962469Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:1f8ebfa0b8e4ffdccf5e3a439de75763187650820fb6f16a9f3f93e9aa8bf2b6","observation_id":"173f1a73-5af3-43d4-82f2-712169e46e84","resolution":{"observed_at":"2026-08-09T18:58:27.119888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:27.008725Z","title":"Gluestick: Robust image matching by sticking points and lines together,","venue":null,"work_id":"3dba06b4-7f69-4c12-9fc7-177f7ea52096","year":2023},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.966124Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:f8ef1f9304d1152007ab607d0ff7eedb2f105ad60a905e7cffe6cd38a1d74a6e","observation_id":"a2681f8e-5e83-4edc-af1c-ba41747117dd","resolution":{"observed_at":"2026-08-09T18:58:27.034770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.997058Z","title":"RoMa: Robust dense feature matching,","venue":null,"work_id":"18571f14-4c5e-4dc7-8cd1-d7953f5bc2f1","year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.969705Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:b7ead3c036297d124fc421ca9c9123df4a1a41f3f6b7d5f81a948659174551cb","observation_id":"b27ab56d-7e53-4404-b36c-d812eee68e9f","resolution":{"observed_at":"2026-08-09T18:58:27.001150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.986217Z","title":"Distinctive image features from scale-invariant key- points,","venue":null,"work_id":"7638209b-9803-46db-826e-1d37792c6004","year":2004},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.973322Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:988be285cef1a9790cd545f3548f0a69348a9dcce1abe1639c2438da2f2911a2","observation_id":"1aa8842f-39b8-43e4-ae78-8a72c1c4d64d","resolution":{"observed_at":"2026-08-09T18:58:26.989791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.976605Z","title":"SURF: Speeded up robust features,","venue":null,"work_id":"20b599cf-2aba-45cb-976c-d1ffa533f184","year":2006},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.976904Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:076d2eac2c380db63e40316eb9beb1f219f7c66c633b01f5260e6771b010531e","observation_id":"3cbd645e-02c0-4c16-95c9-b7c9804b8792","resolution":{"observed_at":"2026-08-09T18:58:26.979724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.965991Z","title":"ORB: An efficient alternative to SIFT or SURF,","venue":null,"work_id":"6b5c7dec-a215-471e-96ef-14f336000179","year":2011},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.980323Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:185c9f084f3d6ad18cead3c19afcd92df34a6e7b78eaec8366c5397b71154973","observation_id":"6f352451-1eac-46df-a6bc-b4f616972f0a","resolution":{"observed_at":"2026-08-09T18:58:26.969800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.956000Z","title":"D2-Net: A trainable CNN for joint description and detection of local features,","venue":null,"work_id":"390ec176-9332-4877-b171-1d65facb9de6","year":2019},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.983519Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:069a7defc0d10cc893f70f719b3f0d06458fd25a88db11b4e46905998731e8a8","observation_id":"896d9680-548c-4ae3-b0ff-826d5bd38e00","resolution":{"observed_at":"2026-08-09T18:58:26.959521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.945098Z","title":"LIFT: Learned invariant feature transform,","venue":null,"work_id":"624f9d96-a327-4bd4-bd53-af5c14afea46","year":2016},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.986236Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:873b4e948a6c2c8aeef52cb7e372da07f6615300da1fb024bc6e622a210bc82b","observation_id":"3c73c185-446c-45f5-9651-b40bfb704e87","resolution":{"observed_at":"2026-08-09T18:58:26.948916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.934318Z","title":"A single correspondence is enough: Robust global registration to avoid degeneracy in urban environments,","venue":null,"work_id":"2164525e-b635-4036-8016-934e09e44389","year":2022},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.989892Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:f54d356205e20f2285f07b028ad625ee185e0bed640ee89fc73246007d418113","observation_id":"d34d29e9-e5bb-4894-acf7-13e261793c30","resolution":{"observed_at":"2026-08-09T18:58:26.938391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.922264Z","title":"Quatro++: Robust global registration exploiting ground segmentation for loop closing in LiDAR SLAM,","venue":null,"work_id":"2e1925a8-cdea-4361-832c-7f8e76bad085","year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.993588Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:cb976a14a135a4a6b71cc40ac5818ec7acc86625e31a4b6c70248969befce5b4","observation_id":"19de5020-9726-4f88-bf0e-ea7031996a65","resolution":{"observed_at":"2026-08-09T18:58:26.926086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.909525Z","title":"Fast approximate nearest neighbors with automatic algorithm configuration","venue":null,"work_id":"b0e7a086-c31c-4cb9-bc66-87412caa2a7f","year":2009},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:25.997438Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:8c1735a69e84d02b6b56049cab2c651a15f4311fc2bba1e488e1ae8f76252eaa","observation_id":"e70a7042-778e-4812-a1ad-d52958142312","resolution":{"observed_at":"2026-08-09T18:58:26.914495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.897062Z","title":"Learning to find good correspondences,","venue":null,"work_id":"f2055d69-f389-488e-8c52-93726e9baf16","year":2018},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.000272Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:b84cdfe5d4665cbb6e0aa24ce405421332c4e434d298557bb1a5a44dbeedf4af","observation_id":"ad7d4188-f4c3-4261-86dc-1107c75f4d93","resolution":{"observed_at":"2026-08-09T18:58:26.900875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.755058Z","title":"Learning two-view correspondences and geometry using order-aware network,","venue":null,"work_id":"ef12998e-35ab-4687-8dd0-8342a3e24542","year":2019},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.003742Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:2f6fd82011b7c3a6a71f0c8476d6ef2705bc0e84a87c99dacf0aeec92c16385d","observation_id":"29867995-26f8-429a-9331-d48fb00d480d","resolution":{"observed_at":"2026-08-09T18:58:26.811575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.655155Z","title":"Handcrafted outlier detection revisited,","venue":null,"work_id":"a46fe59a-4042-4f9c-999b-e0bbe9b18d43","year":2020},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.007495Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:ad2946b6ae50ef87c9d9511684483a9e53e9cdc48b4924c3cd76a959f6cd3537","observation_id":"833a7feb-4e25-4c6c-b671-f95908589d75","resolution":{"observed_at":"2026-08-09T18:58:26.695942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.011135Z","title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.011135Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:69c2b3c8b3bbdf5d10862aa9ec39c1facf807ab423bb4bf4dd42164271753381","observation_id":"2bc04e62-5f19-4ecb-b36f-f5c0e5843914","resolution":{"observed_at":"2026-08-09T18:58:26.011135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.627689Z","title":"Computational optimal transport,","venue":null,"work_id":"0b82f518-959b-405f-a990-23791f3dde51","year":2019},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.014278Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:5ed26e69f987f8272f193e73905b15d2bdf56367ae0f96c4717dca5c5943a54c","observation_id":"c7dc6d0e-7eff-4943-9bc4-1b43d89ded60","resolution":{"observed_at":"2026-08-09T18:58:26.631951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-09T18:58:26.018253Z","title":"Longformer: The long- document transformer,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.018253Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:bdb7219d644a146a6ccc5ce8e8fac45e2e121ce7f662be57959ce462c031baf0","observation_id":"5bca94fb-203f-43e0-a582-de77a047ad1e","resolution":{"observed_at":"2026-08-09T18:58:26.018253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.616071Z","title":"On the computational complexity of self-attention,","venue":null,"work_id":"dee8154a-913d-43a2-b6f1-318f5cc5d76e","year":2023},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.022271Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:861535b395442b2ab2b4244f7e5cc49a9032d4a36772a7177b28a139b1f40e3e","observation_id":"4ac1df84-2ebf-40c2-8791-4f163409f775","resolution":{"observed_at":"2026-08-09T18:58:26.620196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.605146Z","title":"Learning to match features with seeded graph matching network,","venue":null,"work_id":"061f7174-aa0f-42c4-a5ca-098d24bbf91b","year":2021},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.025359Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:7fc729cfb03f6cc438753fb7b4f8f0efbbe15046f26f5f64e7f0922b0037ab64","observation_id":"70920bec-6db6-45fa-96a4-4216e1546f83","resolution":{"observed_at":"2026-08-09T18:58:26.608904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.591312Z","title":"ClusterGNN: Cluster-based coarse-to-fine graph neural network for efficient feature matching,","venue":null,"work_id":"68297e7f-64d1-4e7d-a5ca-e85e27400434","year":2022},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.028489Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:a111a2aefd71d1565b6ec3904cf21b127788afa717ded9bcc5ead60de2ae0f4d","observation_id":"67b33a20-3c93-4583-aee5-ee2ae8178fbb","resolution":{"observed_at":"2026-08-09T18:58:26.595922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14168","last_updated":"2024-08-01T15:56:43Z","snapshot_observed_at":"2026-08-09T01:52:24.869810Z","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":"2026-08-09T18:51:50.068196Z","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":"sha256:0343d9ea3c233655fdadad21ad63ee06d17562e6c7ee632809280ab81be5e442","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.580177Z","title":"RoFormer: En- hanced transformer with rotary position embedding,","venue":null,"work_id":"e7dff788-edd5-42a3-a3d7-1892a5b015b9","year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.035943Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:9a7995b21f4ed91d65f6c250426ce49adfe26704360208294ed26a38662d038b","observation_id":"d0c5c306-aa76-4dd2-897d-e12efe1aa178","resolution":{"observed_at":"2026-08-09T18:58:26.583682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.039613Z","title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.039613Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:23527ea48266e48e010ed9698e5871a7b280e8339deb18040a0dbcfd7039082a","observation_id":"45a3d498-bfdc-4445-a681-7b6cb88d6fec","resolution":{"observed_at":"2026-08-09T18:58:26.039613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12138","last_updated":"2024-10-31T06:38:27Z","snapshot_observed_at":"2026-08-10T05:45:53.322234Z","submitted_at":"2024-02-19T13:38:15Z","title":"Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12138","snapshot_observed_at":"2026-08-09T18:58:26.044023Z","title":"Perceiving longer sequences with bi-directional cross-attention transformers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.044023Z"},"links":{"cited_paper":"/paper/2402.12138","citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:77bcda010862ac92873d63331e70a15e2d7e1e40f4eafd4984f8995e3dd6690b","observation_id":"d587b7b7-026e-4ca8-aa49-842a752ccb3e","resolution":{"observed_at":"2026-08-09T18:58:26.044023Z","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":"2026-08-09T18:58:26.096659Z","title":"A mathematical theory of communication,","venue":null,"work_id":null,"year":1948},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.096659Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:5aafb74f3c9c5d278e459c3c3c1b8d8990defc3b6907b065535bbf3e6f719b6f","observation_id":"96b2c681-6018-4fd0-ac8d-fb3f95cbaad5","resolution":{"observed_at":"2026-08-09T18:58:26.096659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.553236Z","title":"HPatches: A benchmark and evaluation of handcrafted and learned local descrip- tors,","venue":null,"work_id":"0622f510-e36a-4b3b-ae1e-a96a577a568f","year":2017},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.130954Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:53028297c4833a99462c6a576a28c07425a6578bddc91397b93437552bdb404c","observation_id":"d0ae39d8-8528-4023-b525-6b525c56f149","resolution":{"observed_at":"2026-08-09T18:58:26.557615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.541789Z","title":"Match- former: Interleaving attention in transformers for feature matching,","venue":null,"work_id":"01ee2806-0bf6-4ecb-a794-85f80e6e3a3b","year":2022},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.170700Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:be874dc2f34888d50fe407ad9162779f071d1308a9e0f91f7cf6bce71cd60850","observation_id":"d4016660-0cd8-4163-8e41-188e4be1e534","resolution":{"observed_at":"2026-08-09T18:58:26.545808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.529573Z","title":"ASpanFormer: Detector-free image matching with adaptive span transformer,","venue":null,"work_id":"3c2bd655-d012-4f68-a2ee-e5127a5756f0","year":2022},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.201651Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:49c87927809d33ee7cc7929d152e049a23a1cd9dc803d0b36dcb8cf6f5a6119b","observation_id":"2278a300-765f-4b4b-a323-4c98f7333070","resolution":{"observed_at":"2026-08-09T18:58:26.534110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.517497Z","title":"MegaDepth: Learning single-view depth prediction from internet photos,","venue":null,"work_id":"fc9e7523-e9a0-4510-9920-7e4c2d05b0a2","year":2018},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.260090Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:d139be2b1a34be348d8d3eb202196bf7274b0ac136eaa9850d426dc0f10a7034","observation_id":"83c10383-c96b-417f-9a7a-b2b8cf7bb7f0","resolution":{"observed_at":"2026-08-09T18:58:26.521899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.503216Z","title":"Poselib - minimal solvers for camera pose estimation,","venue":null,"work_id":"8d712abe-0a68-4ace-9fc7-611b5f8acd6e","year":2024},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.306750Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:1eebe5e5e4fcad3132f0403ac323191091d856e763f88fab2a8b0fe06f7c611e","observation_id":"ed398a1e-078e-41b8-bb87-7bc74e79030d","resolution":{"observed_at":"2026-08-09T18:58:26.508963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:58:26.314404Z","title":"Hartley and A","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T18:58:26.314404Z"},"links":{"citing_paper":"/paper/2502.00462"},"observation_digest":"sha256:b875fd1298ccd9724dd674e85d488cf33443f5cbc5a2bfdc273f233ffa3b33e4","observation_id":"351ed4a7-a604-42a0-840c-38892d380827","resolution":{"observed_at":"2026-08-09T18:58:26.314404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.00462","last_updated":"2025-02-01T15:43:03Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T18:51:50.068196Z","submitted_at":"2025-02-01T15:43:03Z","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":43},"total_outbound_references":58},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2502.00462."}