{"as_of":"2026-08-06T13:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9fa65514de003066549693e215a514956627a81e849c8906e7d0367be3433c4","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":910,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T20:10:05.205162Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":4,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2411.19093","last_updated":"2026-05-30T15:30:28Z","snapshot_observed_at":"2026-07-06T19:58:31.184255Z","submitted_at":"2024-11-28T12:13:46Z","title":"Seeing SDG 6 from space: local-scale monitoring of piped water and sewage system access across Africa using satellite imagery and self-supervised learning","version":5},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-23T17:16:11.599623Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2411.19093"},"observation_digest":"sha256:64881a4328f1281a1e9b786c77a2623d286522eff0abb0980092d29e0aa7476f","observation_id":"8fa472b4-1096-44f6-aebc-468bf59c8337","resolution":{"observed_at":"2026-05-23T17:18:14.596527Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2502.02779","last_updated":"2026-04-21T17:21:08Z","snapshot_observed_at":"2026-07-06T20:31:17.809419Z","submitted_at":"2025-02-04T23:42:18Z","title":"3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-23T03:26:46.665351Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2502.02779"},"observation_digest":"sha256:dbf8d6a7eb285897dfd169d6ddd4185e55d918ccd9285a1c7c3ebb70cc865b95","observation_id":"55877c7a-c5d2-4b5e-8450-75b782632df0","resolution":{"observed_at":"2026-05-23T03:27:27.046641Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2508.06248","last_updated":"2026-05-11T10:15:54Z","snapshot_observed_at":"2026-07-06T22:09:57.861336Z","submitted_at":"2025-08-08T12:03:56Z","title":"Deepfake Detection that Generalizes Across Benchmarks","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-18T23:47:27.729587Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2508.06248"},"observation_digest":"sha256:292316e6182c745a235d2294e5c076a6edd74b40e1921c51d56fbfe03053713a","observation_id":"b7ff6680-af79-413a-8fff-13fa87622959","resolution":{"observed_at":"2026-05-18T23:51:55.062617Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T20:08:56.016306Z","title":"Sim ´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.11163","last_updated":"2025-08-15T02:30:20Z","snapshot_observed_at":"2026-08-05T20:08:49.104263Z","submitted_at":"2025-08-15T02:30:20Z","title":"MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T20:08:56.016306Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2508.11163"},"observation_digest":"sha256:562a260d8a7602e644b2155934772080f7072d98fd949a8b6b00c0af6ee9efbd","observation_id":"0b8aa0d4-6938-44dc-a674-241ebe62c9cd","resolution":{"observed_at":"2026-08-05T20:08:56.016306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T20:10:05.205162Z","title":"Sim ´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.11167","last_updated":"2025-08-26T08:55:26Z","snapshot_observed_at":"2026-08-05T20:09:59.107480Z","submitted_at":"2025-08-15T02:35:56Z","title":"VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T20:10:05.205162Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2508.11167"},"observation_digest":"sha256:29f766adc80b4710490d6217d2ff4cb8aa4a70d66e72c927009702b149499815","observation_id":"09b92571-f072-4d47-828c-9f186b734b1f","resolution":{"observed_at":"2026-08-05T20:10:05.205162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T16:41:41.779734Z","title":"Sim´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.18067","last_updated":"2025-08-25T14:22:57Z","snapshot_observed_at":"2026-08-06T04:45:07.131331Z","submitted_at":"2025-08-25T14:22:57Z","title":"Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T16:41:41.779734Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2508.18067"},"observation_digest":"sha256:28451f42664069cd5a24ee836a3a386d324d3a6fa472edc868623db8746ceaf3","observation_id":"cc534ef3-b65a-4e55-81d3-93c0862876d2","resolution":{"observed_at":"2026-08-05T16:41:41.779734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2508.20909","last_updated":"2026-05-08T06:39:51Z","snapshot_observed_at":"2026-08-04T15:16:30.376993Z","submitted_at":"2025-08-28T15:38:50Z","title":"Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T20:22:41.555806Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2508.20909"},"observation_digest":"sha256:e9834d3bfd0a9239a202f661c092e1a948e8e9409295e2f111c1959838162cdb","observation_id":"18397311-5588-4a30-bede-3eb00d3eb645","resolution":{"observed_at":"2026-05-18T20:22:50.696311Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T13:14:15.332805Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.00833","last_updated":"2025-08-31T13:06:37Z","snapshot_observed_at":"2026-08-05T13:14:14.941611Z","submitted_at":"2025-08-31T13:06:37Z","title":"SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T13:14:15.332805Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.00833"},"observation_digest":"sha256:208a6809a0ee517d481cf5b46ce91dbb98bfc598bc16c8ebcedf04b562da5aba","observation_id":"fbd06ad7-5de4-40a3-960a-1aad392da16e","resolution":{"observed_at":"2026-08-05T13:14:15.332805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T12:45:29.498792Z","title":"Siméoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.01360","last_updated":"2025-09-01T10:59:39Z","snapshot_observed_at":"2026-08-06T11:46:03.801729Z","submitted_at":"2025-09-01T10:59:39Z","title":"M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T12:45:29.498792Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.01360"},"observation_digest":"sha256:837b1259b650acc5c961800bae19ea484abd93c75db55ad1c0c2291dcb710dcf","observation_id":"f55aa540-f0de-4be5-ba1e-6c35a8f6240b","resolution":{"observed_at":"2026-08-05T12:45:29.498792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2509.01878","last_updated":"2026-04-09T01:40:08Z","snapshot_observed_at":"2026-07-06T22:21:58.114759Z","submitted_at":"2025-09-02T01:51:31Z","title":"AI-Driven Marine Robotics: Emerging Trends in Underwater Perception and Ecosystem Monitoring","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-18T20:30:33.239934Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.01878"},"observation_digest":"sha256:e915b6d967a63a6fa32f64cdbf8a827afa3fb7a7516d6a2f40bcf96505de1056","observation_id":"db84ad64-d230-43e9-a50f-07d7f5184446","resolution":{"observed_at":"2026-05-18T20:31:50.462746Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2509.02560","last_updated":"2025-11-09T15:12:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-02T17:54:21Z","title":"FastVGGT: Training-Free Acceleration of Visual Geometry Transformer","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T23:36:06.870759Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.02560"},"observation_digest":"sha256:3af3509c5819ba878ee0a63cfe869aa686d5163a21bfcd9b6ac3a045ba9375ef","observation_id":"ed74ebb5-da7a-4143-8ffc-b80935ddd53b","resolution":{"observed_at":"2026-05-15T23:36:06.987511Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T10:57:48.267016Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03421","last_updated":"2025-09-03T15:48:57Z","snapshot_observed_at":"2026-08-05T10:57:45.233407Z","submitted_at":"2025-09-03T15:48:57Z","title":"Generalist versus Specialist Vision Foundation Models for Ocular Disease and Oculomics","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:57:48.267016Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.03421"},"observation_digest":"sha256:988eff5e57791bb1939d428597a14c32db6c2e7522d8c7b102cc7933f20f24a6","observation_id":"1ecad42c-2b37-4bec-b2f3-0cae7d3c80f5","resolution":{"observed_at":"2026-08-05T10:57:48.267016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T00:05:47.846026Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.06155","last_updated":"2025-09-07T17:55:03Z","snapshot_observed_at":"2026-08-05T00:05:44.790684Z","submitted_at":"2025-09-07T17:55:03Z","title":"UniVerse-1: Unified Audio-Video Generation via Stitching of Experts","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T00:05:47.846026Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.06155"},"observation_digest":"sha256:108a65485389806d19059f2a355eb3ceffb115045b495de770c8afb181a977d1","observation_id":"8112021b-dd7f-4a21-901a-fd2e6726d6e9","resolution":{"observed_at":"2026-08-05T00:05:47.846026Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T11:31:41.917100Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.06990","last_updated":"2025-09-02T18:48:14Z","snapshot_observed_at":"2026-08-05T11:31:41.542849Z","submitted_at":"2025-09-02T18:48:14Z","title":"DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T11:31:41.917100Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.06990"},"observation_digest":"sha256:5df9b08a5d1cd56924fc85fba6977886652b830929255e03e9b3024b5eef99fb","observation_id":"086f93d2-2f9b-429d-8f4c-6b0307f3dac7","resolution":{"observed_at":"2026-08-05T11:31:41.917100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T18:55:35.641588Z","title":"Sim ´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.09572","last_updated":"2025-09-11T16:08:43Z","snapshot_observed_at":"2026-08-04T18:55:35.035693Z","submitted_at":"2025-09-11T16:08:43Z","title":"PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T18:55:35.641588Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.09572"},"observation_digest":"sha256:5fc7272a0ae4f18a464ff7d4d0a7cec3a385ece24c66a77d62f6897f8f3b9b1b","observation_id":"0dee32a7-b8c2-4df6-95e2-86fe16e57298","resolution":{"observed_at":"2026-08-04T18:55:35.641588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T17:10:04.387734Z","title":"Sim ´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11082","last_updated":"2025-09-14T04:19:52Z","snapshot_observed_at":"2026-08-04T17:10:03.867075Z","submitted_at":"2025-09-14T04:19:52Z","title":"Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T17:10:04.387734Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.11082"},"observation_digest":"sha256:024d5d2d7815643061e21ef7db401e645e91a696d8eeea4dd4f06a8400464d3e","observation_id":"3055d09b-c43c-4f66-9681-9b489e28bd83","resolution":{"observed_at":"2026-08-04T17:10:04.387734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T15:43:09.085047Z","title":"Siméoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.18919","last_updated":"2026-05-26T11:07:50Z","snapshot_observed_at":"2026-08-04T15:43:04.927279Z","submitted_at":"2025-09-23T12:35:32Z","title":"Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T15:43:09.085047Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.18919"},"observation_digest":"sha256:64a346bc71c36292bc54b29a769991a725e06fba57e322627321a147322b9365","observation_id":"2569de7a-fb28-405e-b8b1-964876bc1b24","resolution":{"observed_at":"2026-08-04T15:43:09.085047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2509.22769","last_updated":"2026-05-21T14:11:11Z","snapshot_observed_at":"2026-08-01T22:52:26.594198Z","submitted_at":"2025-09-26T17:59:16Z","title":"PartCo: Part-Level Correspondence Priors Enhance Category Discovery","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-22T13:31:12.201742Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2509.22769"},"observation_digest":"sha256:3da65047b463e0c916436651f880e664e0973f946a855fcd3f4b83ca0d3f4b67","observation_id":"1231fcf8-febf-4fd7-9b86-f3e0b4dc96c0","resolution":{"observed_at":"2026-05-22T13:31:36.026328Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2510.00520","last_updated":"2026-05-20T09:00:31Z","snapshot_observed_at":"2026-08-01T10:25:17.270308Z","submitted_at":"2025-10-01T05:09:48Z","title":"CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-21T21:41:55.451141Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.00520"},"observation_digest":"sha256:04f62262091cc56d71031f4512838d02d664ba5ca5558bd7a20ad9e8440ec5a2","observation_id":"fb226236-97c9-4ff6-9a21-10254b929a27","resolution":{"observed_at":"2026-05-21T21:44:22.749484Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T11:31:29.827005Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.04547","last_updated":"2026-07-08T06:28:12Z","snapshot_observed_at":"2026-08-05T18:52:45.747562Z","submitted_at":"2025-10-06T07:27:46Z","title":"Activation Quantization of Vision Encoders Needs Prefixing Registers","version":5},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T11:31:29.827005Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.04547"},"observation_digest":"sha256:47e59988b132d39b2d03119617bd184534a5cd7fb45c5a07dcf95c1c6a6aa150","observation_id":"6604e520-b58a-4334-8838-4dcff8208d4f","resolution":{"observed_at":"2026-08-04T11:31:29.827005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2510.07191","last_updated":"2026-04-25T19:09:40Z","snapshot_observed_at":"2026-08-01T18:20:54.109595Z","submitted_at":"2025-10-08T16:25:04Z","title":"Resolution scaling governs DINOv3 transfer performance in chest radiograph classification","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-18T09:05:29.448447Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.07191"},"observation_digest":"sha256:d4172c77e6917473a27ee42fbfc62549fee92a0ba93f0440ba5956f2ce98b26a","observation_id":"8b5ea262-818a-40cd-bc93-250db9f6ea2d","resolution":{"observed_at":"2026-05-18T09:06:09.131842Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T10:37:39.470308Z","title":"Dinov3.arXiv preprint arXiv:2508.10104,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.09606","last_updated":"2026-05-26T09:02:06Z","snapshot_observed_at":"2026-08-04T10:37:28.653177Z","submitted_at":"2025-10-10T17:59:46Z","title":"SpaceVista: All-Scale Visual Spatial Reasoning from mm to km","version":2},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-04T10:37:39.470308Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.09606"},"observation_digest":"sha256:d4781c4a7592febbf0ad3302c496ef1f6e5dd8dab14bb1e08574fdbf62ab4dc7","observation_id":"693385d1-de7c-4db3-b244-c16a6e954a9f","resolution":{"observed_at":"2026-08-04T10:37:39.470308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2510.12796","last_updated":"2025-12-18T07:25:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-14T17:59:47Z","title":"DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-17T06:48:00.943591Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.12796"},"observation_digest":"sha256:95344a7a333c4f6ddf07de06305614f526ad3a81d573ac4a63a575639a6e7c57","observation_id":"6048d556-e9be-4fe7-9a63-09ce43de4dec","resolution":{"observed_at":"2026-05-17T06:48:01.091554Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2510.15849","last_updated":"2026-07-10T12:14:23Z","snapshot_observed_at":"2026-08-04T09:21:19.931286Z","submitted_at":"2025-10-17T17:42:28Z","title":"Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-18T06:03:45.684267Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.15849"},"observation_digest":"sha256:5d214290b244833de67b8f2ae1074e72de1e3000f93a0c23cd5493f64f02a9ef","observation_id":"b2138c60-baf3-4c57-ad32-e78f4d503726","resolution":{"observed_at":"2026-05-18T06:05:57.473592Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T09:21:21.558253Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.15849","last_updated":"2026-07-10T12:14:23Z","snapshot_observed_at":"2026-08-04T09:21:19.931286Z","submitted_at":"2025-10-17T17:42:28Z","title":"Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T09:21:21.558253Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.15849"},"observation_digest":"sha256:7bcc046b8b025d31cf2f91f850eb4519aff58ac481f7fa0836f052979b0a65df","observation_id":"c9ef338c-3cf7-4b3e-bd4b-80cf25fa87eb","resolution":{"observed_at":"2026-08-04T09:21:21.558253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T09:03:09.336349Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.17700","last_updated":"2026-05-29T13:56:08Z","snapshot_observed_at":"2026-08-05T01:39:56.711121Z","submitted_at":"2025-10-20T16:15:03Z","title":"Elastic ViTs from Pretrained Models without Retraining","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T09:03:09.336349Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.17700"},"observation_digest":"sha256:b84e0e6995b677727d431878d85b1b7c84cb01eaf43c2dbb17816270b5af90d7","observation_id":"cfb4958b-b9fd-4ceb-b9ec-f7dbf8162639","resolution":{"observed_at":"2026-08-04T09:03:09.336349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T09:12:38.166432Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.17917","last_updated":"2026-06-30T11:23:39Z","snapshot_observed_at":"2026-08-04T09:12:31.464731Z","submitted_at":"2025-10-20T02:00:12Z","title":"Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-04T09:12:38.166432Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.17917"},"observation_digest":"sha256:72622653843815d85178c74897ee34707a43cc5681f93e83b2b9681d86ac6f67","observation_id":"030bf170-f131-478c-a790-ffc5894cb964","resolution":{"observed_at":"2026-08-04T09:12:38.166432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2510.18457","last_updated":"2026-04-23T08:02:03Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-21T09:30:45Z","title":"VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-18T05:22:05.125849Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.18457"},"observation_digest":"sha256:b3100273524112b3604f09a078a0db2d2f44e12e67ccc87264b50191e2136499","observation_id":"c184aa1d-fd04-474f-b800-6dfcd6d2aa0d","resolution":{"observed_at":"2026-05-18T05:22:23.995050Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T08:20:01.265096Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.21605","last_updated":"2026-06-17T16:14:08Z","snapshot_observed_at":"2026-08-06T00:04:26.344716Z","submitted_at":"2025-10-24T16:10:09Z","title":"S3OD: Towards Generalizable Salient Object Detection with Synthetic Data","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-04T08:20:01.265096Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.21605"},"observation_digest":"sha256:98b5001dbdb97239af69a2c3fee90740b0bbe4473170a6964fecfc8d1056b0be","observation_id":"2165d00d-4f98-4d24-9e37-4aaf47718d28","resolution":{"observed_at":"2026-08-04T08:20:01.265096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T07:54:10.509113Z","title":", author Vo, H.V","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.23798","last_updated":"2026-06-15T21:18:53Z","snapshot_observed_at":"2026-08-04T07:53:41.383950Z","submitted_at":"2025-10-27T19:29:14Z","title":"A geometric and deep learning reproducible pipeline for monitoring floating anthropogenic debris in urban rivers using in situ cameras","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-04T07:54:10.509113Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2510.23798"},"observation_digest":"sha256:fd91b7d542ffc626613d269d896d2e83e127f52efb3ec340cf8ad7fe4e2bcc37","observation_id":"56b3683f-8dcc-44a3-a193-e76ccbb82679","resolution":{"observed_at":"2026-08-04T07:54:10.509113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2511.02830","last_updated":"2026-04-18T20:04:38Z","snapshot_observed_at":"2026-07-06T22:34:53.346702Z","submitted_at":"2025-11-04T18:58:03Z","title":"Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T00:53:24.323059Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.02830"},"observation_digest":"sha256:27403ad036214c4e651b6e067f60524f6e8f09406641d0e7c6ea0c0a9be33b8d","observation_id":"8b71d498-3164-4406-b33d-d690ff747651","resolution":{"observed_at":"2026-05-18T00:55:35.242807Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T23:34:15.815600Z","title":"arXiv preprint arXiv:2508.101043(4) (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.05150","last_updated":"2026-07-02T15:10:58Z","snapshot_observed_at":"2026-08-03T23:34:12.277786Z","submitted_at":"2025-11-07T11:05:36Z","title":"Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T23:34:15.815600Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.05150"},"observation_digest":"sha256:513c8e9bc504c5c009bb7db03eef6e8c8a18bcd7f6ad35aaa27a6738d5d38202","observation_id":"ee5451e6-3aa8-4ce2-a2c4-90d347b1c3b7","resolution":{"observed_at":"2026-08-03T23:34:15.815600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T23:17:32.936917Z","title":"Sim ´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.06644","last_updated":"2026-06-08T06:25:46Z","snapshot_observed_at":"2026-08-06T03:52:19.054451Z","submitted_at":"2025-11-10T02:42:08Z","title":"UniADC: A Unified Framework for Anomaly Detection and Classification","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T23:17:32.936917Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.06644"},"observation_digest":"sha256:9ae796e33a302262187beaa35c48c0286c2d018a4e097e32e3294fa70b8df0bb","observation_id":"db4ce52f-d9db-4c44-9deb-47fc22d613b8","resolution":{"observed_at":"2026-08-03T23:17:32.936917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2511.08544","last_updated":"2025-11-14T08:38:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-11-11T18:21:55Z","title":"LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-16T07:22:59.854042Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.08544"},"observation_digest":"sha256:a8e5786b21e2a74e160851cd994fb7b89734d375f8ca02868150ea003bbff688","observation_id":"5b27e588-645e-4af4-a313-ec1edec4893a","resolution":{"observed_at":"2026-05-16T07:23:00.016578Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T22:16:16.783061Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.11270","last_updated":"2026-06-30T13:53:11Z","snapshot_observed_at":"2026-08-05T00:11:28.787409Z","submitted_at":"2025-11-14T13:05:10Z","title":"{\\Phi}eat: Physically Grounded Material Feature Representation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T22:16:16.783061Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.11270"},"observation_digest":"sha256:6c9f4e892e9cc61ead7d73c9122ed50c804b5460ebda83ac942f1fd241863689","observation_id":"64af748a-f9e2-4b95-bfba-45e178038f68","resolution":{"observed_at":"2026-08-03T22:16:16.783061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2511.11435","last_updated":"2026-04-09T06:58:23Z","snapshot_observed_at":"2026-08-02T12:13:29.732013Z","submitted_at":"2025-11-14T16:03:10Z","title":"The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Models","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-17T21:52:26.944212Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.11435"},"observation_digest":"sha256:1c8ec866465d76dd062ed1122905b79b6e4aceadda7ac453f118fc2dbbf19e2d","observation_id":"6ab52e01-b898-4d54-a9fd-d1bc6072cc10","resolution":{"observed_at":"2026-05-17T21:55:20.311565Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T22:13:46.241078Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.11470","last_updated":"2026-07-14T08:32:04Z","snapshot_observed_at":"2026-08-03T22:13:41.146869Z","submitted_at":"2025-11-14T16:42:03Z","title":"Sat2RealCity: Geometry-Aware and Appearance-Controllable 3D Urban Generation from Satellite Imagery","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T22:13:46.241078Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.11470"},"observation_digest":"sha256:e642059a8b47ec21cdccd65d9c19f15cd0d8f6046440b9fd7f10f6bc804cd271","observation_id":"e9be355a-964d-433a-ab75-2019a2ff43a8","resolution":{"observed_at":"2026-08-03T22:13:46.241078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2511.12606","last_updated":"2026-04-21T06:06:34Z","snapshot_observed_at":"2026-07-06T22:35:55.936840Z","submitted_at":"2025-11-16T14:04:12Z","title":"Pixels or Positions? Benchmarking Modalities in Group Activity Recognition","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-17T21:45:20.428547Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.12606"},"observation_digest":"sha256:d9c2d9708a0a62ac38d4250f28369d6bfba74e9f65e8b1e03c14e89ff80c5e0a","observation_id":"7b8f6809-21fa-41a1-bcb1-b3e48cf9e0a7","resolution":{"observed_at":"2026-05-17T21:50:19.718324Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T21:24:49.186860Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.15706","last_updated":"2026-07-06T10:31:24Z","snapshot_observed_at":"2026-08-03T21:24:47.092177Z","submitted_at":"2025-11-19T18:59:38Z","title":"RoMa v2: Harder Better Faster Denser Feature Matching","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T21:24:49.186860Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.15706"},"observation_digest":"sha256:380255a1053ac8592055baae834656bcb29fdbfae578d641dda8c22905ba22db","observation_id":"d2e9cd46-da43-4cc1-adc9-5786cf7c55c3","resolution":{"observed_at":"2026-08-03T21:24:49.186860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T21:07:14.817049Z","title":"V ., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V ., Szafraniec, M., Yi, S., Ramamonjisoa, M., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.16870","last_updated":"2026-05-25T20:19:17Z","snapshot_observed_at":"2026-08-06T07:55:10.087277Z","submitted_at":"2025-11-21T00:37:04Z","title":"Align & Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment","version":3},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-03T21:07:14.817049Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.16870"},"observation_digest":"sha256:f22188bab238768bd62bcbf203aafad2afd2b13e949663efed63c293fee864db","observation_id":"403e5516-7a4b-44d8-97c0-d86d50677a92","resolution":{"observed_at":"2026-08-03T21:07:14.817049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T21:03:43.448478Z","title":"Di- nov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.17221","last_updated":"2026-06-11T16:41:57Z","snapshot_observed_at":"2026-08-03T21:03:38.837593Z","submitted_at":"2025-11-21T13:05:42Z","title":"QueryOcc: Query-based Self-Supervision for 3D Semantic Occupancy","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T21:03:43.448478Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.17221"},"observation_digest":"sha256:f40e3bee9a333a14150aa342cfef3cdeed8afb3571ee88721734ee62894d9629","observation_id":"237f50d5-c6a6-4ceb-a631-dfc883569ba5","resolution":{"observed_at":"2026-08-03T21:03:43.448478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T20:30:35.520090Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.19778","last_updated":"2026-06-30T01:28:09Z","snapshot_observed_at":"2026-08-05T10:47:35.903014Z","submitted_at":"2025-11-24T23:10:15Z","title":"Phase-Aligned RoPE for Mixed-Resolution Diffusion Transformer","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-03T20:30:35.520090Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.19778"},"observation_digest":"sha256:afc3cfaae08870bc90255cc0134291e7d84f7cc13fef6edd612475576aa002ab","observation_id":"1a2304ef-5e90-4ab0-86d5-1e419724144f","resolution":{"observed_at":"2026-08-03T20:30:35.520090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2511.22039","last_updated":"2026-04-14T12:13:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-11-27T02:48:45Z","title":"SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-17T05:26:34.859975Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.22039"},"observation_digest":"sha256:e2dd82b08875bfe0799550c3c7859409e4edbfa76d2b3b1770fcc246f9da1d8e","observation_id":"d2e00fd4-96a9-4e24-a333-5c2990bffc3c","resolution":{"observed_at":"2026-05-17T05:29:04.967759Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2511.22958","last_updated":"2026-04-28T19:14:29Z","snapshot_observed_at":"2026-07-06T22:37:12.421372Z","submitted_at":"2025-11-28T08:03:46Z","title":"Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-17T04:17:57.951707Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2511.22958"},"observation_digest":"sha256:4e3227cfde5ebe6edf00606fe1c57e39d8a2e9297bb55722d1bbdcc7508420b4","observation_id":"dfdd92c0-131f-498b-9d25-7aa40c51088a","resolution":{"observed_at":"2026-05-17T04:19:00.395837Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.00961","last_updated":"2026-04-03T07:09:55Z","snapshot_observed_at":"2026-07-06T22:37:21.536027Z","submitted_at":"2025-11-30T16:22:27Z","title":"Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-17T02:52:22.919967Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.00961"},"observation_digest":"sha256:128469656dd63f869544ee7098a0dddabf0814324b996db5df74e55160cd3410","observation_id":"31621c73-e55f-4265-9a93-de2c6b4c2045","resolution":{"observed_at":"2026-05-17T02:53:54.876698Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.03370","last_updated":"2026-04-10T02:11:22Z","snapshot_observed_at":"2026-07-31T22:38:28.096192Z","submitted_at":"2025-12-03T02:06:09Z","title":"ShelfGaussian: Shelf-Supervised Open-Vocabulary Gaussian-based 3D Scene Understanding","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-17T03:24:32.577420Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.03370"},"observation_digest":"sha256:fcf74dd5a93ef0f45da743d7604eb407eab161f2552ece2135c64b2240ecfc8b","observation_id":"02b1417e-253f-4910-87a0-a6d4ce7991e8","resolution":{"observed_at":"2026-05-17T03:28:58.119925Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.04021","last_updated":"2026-04-28T10:44:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-03T17:59:05Z","title":"C3G: Learning Compact 3D Representations with 2K Gaussians","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-17T02:06:54.730272Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.04021"},"observation_digest":"sha256:cdb0dcc6d1c25b481320bbf25627f1ec7ec45a48b3efb3ec5c49d510fae88e99","observation_id":"0081e8c6-f88e-4cde-910e-c815499703f2","resolution":{"observed_at":"2026-05-17T02:08:51.295886Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.07527","last_updated":"2026-04-08T08:18:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-08T13:01:12Z","title":"From Orbit to Ground: Generative City Photogrammetry from Extreme Off-Nadir Satellite Images","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-17T00:57:35.745943Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.07527"},"observation_digest":"sha256:a7871a0f3a8ff6cd184b885cee2d2b8617be25320167cb4fed3185481233d179","observation_id":"f43ce3e8-0197-42ce-bb20-98599db06f26","resolution":{"observed_at":"2026-05-17T00:58:46.324965Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T17:52:11.789138Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.07806","last_updated":"2026-05-31T08:21:33Z","snapshot_observed_at":"2026-08-03T17:52:04.176106Z","submitted_at":"2025-12-08T18:39:27Z","title":"Multi-view Pyramid Transformer: Look Coarser to See Broader","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T17:52:11.789138Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.07806"},"observation_digest":"sha256:c7b5a28a1faff91ac3a5c6f47f559d4a6c003fd9dd3e0a292245a4256f58bcf6","observation_id":"6b6c1676-a0be-4151-816d-08a85c14284c","resolution":{"observed_at":"2026-08-03T17:52:11.789138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T17:39:46.607876Z","title":"what it can create, it may not understand","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.08854","last_updated":"2026-07-03T17:29:52Z","snapshot_observed_at":"2026-08-03T17:39:43.962448Z","submitted_at":"2025-12-09T17:47:28Z","title":"Is Generation Required for Data-Efficient Perception?","version":3},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-03T17:39:46.607876Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.08854"},"observation_digest":"sha256:af2b6096958a24532de7f19930b83f00d37edd3ef015d87c5c8278101dae4e14","observation_id":"b85b3468-59e9-442f-be55-da40c0a36691","resolution":{"observed_at":"2026-08-03T17:39:46.607876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.09056","last_updated":"2026-04-12T09:52:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-09T19:16:28Z","title":"ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-16T23:43:07.754702Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.09056"},"observation_digest":"sha256:33a5d8c1380ac7121ad3e43f318f0763744a148feb191705d2776d48a94bbc3d","observation_id":"492f5b9c-66dc-44fa-b53d-5ff0feaa3cfb","resolution":{"observed_at":"2026-05-16T23:43:42.037967Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.10955","last_updated":"2026-04-30T17:59:43Z","snapshot_observed_at":"2026-08-04T18:59:53.204146Z","submitted_at":"2025-12-11T18:59:56Z","title":"Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-16T22:52:41.592714Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.10955"},"observation_digest":"sha256:129159efa98ad5e161d47a763466a0c567d506e9de0955c003b44057811d5e0d","observation_id":"7e05af4e-9ee9-40f3-9e1c-d74cd159069c","resolution":{"observed_at":"2026-05-16T22:53:38.244109Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.11016","last_updated":"2026-05-07T10:04:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-11T18:03:30Z","title":"SoccerMaster: A Vision Foundation Model for Soccer Understanding","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-16T23:08:08.584111Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.11016"},"observation_digest":"sha256:32d22e37ccc5e11f3934a2abdb4d47c4b40a90f69923094c78f7666612e0c997","observation_id":"160684e0-1ec8-49b0-955b-bd703b05026a","resolution":{"observed_at":"2026-05-09T02:27:20.912025Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T16:36:36.378043Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.12887","last_updated":"2026-05-26T20:05:22Z","snapshot_observed_at":"2026-08-03T16:36:30.308430Z","submitted_at":"2025-12-15T00:01:19Z","title":"Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T16:36:36.378043Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.12887"},"observation_digest":"sha256:558efe43a8794930792a91a2e684cc13a5f812dd1f88a34528df0ce7b792e5c5","observation_id":"a647ab63-dde3-4f63-8de1-691c7f324188","resolution":{"observed_at":"2026-08-03T16:36:36.378043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T16:25:58.357377Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.13677","last_updated":"2026-07-31T15:26:54Z","snapshot_observed_at":"2026-08-05T23:14:18.016086Z","submitted_at":"2025-12-15T18:58:18Z","title":"JoVA: Unified Multimodal Learning for Joint Video-Audio Generation and Editing","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T16:25:58.357377Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.13677"},"observation_digest":"sha256:0f464423271f5c7c05e553ff7ae4bc9562e291abfb686d5e6f4c9da7ae70a173","observation_id":"da686c8d-b660-4472-81d4-5c950e054d7b","resolution":{"observed_at":"2026-08-03T16:25:58.357377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.14692","last_updated":"2025-12-16T18:58:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-16T18:58:28Z","title":"Native and Compact Structured Latents for 3D Generation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-21T05:20:42.300247Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.14692"},"observation_digest":"sha256:f36e03b29645c93b74b8b837c01b5fa1056d1bd45c01cde163dc8724aeb95d93","observation_id":"07180ba0-d5ae-4314-b973-d4ccf495a314","resolution":{"observed_at":"2026-05-21T05:20:42.429791Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.15577","last_updated":"2026-04-20T23:10:50Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-17T16:36:16Z","title":"MoonSeg3R: Monocular Online Zero-Shot Segment Anything in 3D with Reconstructive Foundation Priors","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-16T21:36:51.329792Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.15577"},"observation_digest":"sha256:1de19874cbd26261d7ce283fc15b97b1a15de19b97b26bb21aae302e05c41270","observation_id":"3297648f-e5dd-450b-b824-745e08929433","resolution":{"observed_at":"2026-05-16T21:38:34.028822Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T15:27:19.710548Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.16919","last_updated":"2026-06-03T07:14:50Z","snapshot_observed_at":"2026-08-04T02:28:30.870384Z","submitted_at":"2025-12-18T18:59:57Z","title":"DVGT: Driving Visual Geometry Transformer","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T15:27:19.710548Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.16919"},"observation_digest":"sha256:ae97bf5ad974eaf2be1643aeae1f32ab5930a64feac2993e631ce8d3f942d5c3","observation_id":"9aa87c0c-c621-4ee5-8f36-1e3f2c300beb","resolution":{"observed_at":"2026-08-03T15:27:19.710548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.17492","last_updated":"2026-04-28T06:26:52Z","snapshot_observed_at":"2026-07-06T22:39:33.919723Z","submitted_at":"2025-12-19T12:03:05Z","title":"MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-16T20:49:11.961079Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.17492"},"observation_digest":"sha256:d3738960016f4f6d719c528fe111fa5066177cadfac7be647f15f803e05d042d","observation_id":"7a516cf8-d4a3-4bf8-93d9-7264b21aa0a2","resolution":{"observed_at":"2026-05-16T20:51:15.275262Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.17817","last_updated":"2026-05-04T21:21:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-12-19T17:22:35Z","title":"Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-16T20:34:16.666956Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.17817"},"observation_digest":"sha256:205a7e222dcd1ad8651d152135760d96e1ad4f26c324b278b316ca04bdc4a985","observation_id":"a8c005e7-5a87-47b4-808c-86c8990ac397","resolution":{"observed_at":"2026-05-16T20:38:24.853538Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.20157","last_updated":"2026-04-07T16:17:58Z","snapshot_observed_at":"2026-08-04T03:25:25.926025Z","submitted_at":"2025-12-23T08:37:11Z","title":"SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-16T20:21:47.430865Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.20157"},"observation_digest":"sha256:3590af0c639575b1c8a3570a13e4886e38cd9a8ecb2c57662dd22978a4eae8d4","observation_id":"84491694-4222-4b63-a73e-8e77fa4a39fc","resolution":{"observed_at":"2026-05-16T20:23:23.747485Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.20538","last_updated":"2026-05-21T14:54:40Z","snapshot_observed_at":"2026-07-06T22:39:58.137482Z","submitted_at":"2025-12-23T17:29:08Z","title":"AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-22T12:33:14.120076Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.20538"},"observation_digest":"sha256:0c33e29f45eaeb9664fca74d2301ec7ca5e67345bad3070d9fa55054a9c4e77c","observation_id":"409e1d96-c49c-4808-b728-e9457e084840","resolution":{"observed_at":"2026-05-22T12:34:52.503487Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T14:24:50.001185Z","title":"DINOv3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.20606","last_updated":"2026-06-29T06:12:05Z","snapshot_observed_at":"2026-08-03T14:24:48.216973Z","submitted_at":"2025-12-23T18:54:10Z","title":"Probing and Leveraging Video Diffusion Transformer Features for Robust Point Tracking","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T14:24:50.001185Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.20606"},"observation_digest":"sha256:73a4d819ec0e2dbbd6eaf9e92e9b59e0b397c75eed6f8bb1e32b93481b30d53d","observation_id":"49177057-4adf-4cc4-ad48-879945916bd6","resolution":{"observed_at":"2026-08-03T14:24:50.001185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T14:08:22.945466Z","title":"Sim \\'e oni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.21545","last_updated":"2026-06-27T07:08:38Z","snapshot_observed_at":"2026-08-05T09:49:54.219121Z","submitted_at":"2025-12-25T07:34:38Z","title":"EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-03T14:08:22.945466Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.21545"},"observation_digest":"sha256:f9f9a3b7e5395383ff906b9f1cb8749b1ed787fc8dfd8569a7bb2f473666296a","observation_id":"1cd0e8c4-7e8c-4876-8e01-6ce1ccf72642","resolution":{"observed_at":"2026-08-03T14:08:22.945466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.22647","last_updated":"2026-04-06T10:25:52Z","snapshot_observed_at":"2026-07-06T22:40:12.666349Z","submitted_at":"2025-12-27T16:55:21Z","title":"FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-16T19:00:03.367921Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.22647"},"observation_digest":"sha256:af7ad8d476d5167925ffd6321350f537cb69a46dd4c9229995bfbeb8b8ac8f4a","observation_id":"c1a1dc19-950c-4e04-ac01-2589a76aac09","resolution":{"observed_at":"2026-05-16T19:01:11.689919Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2512.24497","last_updated":"2026-05-18T09:26:59Z","snapshot_observed_at":"2026-07-31T22:57:41.701920Z","submitted_at":"2025-12-30T22:50:03Z","title":"What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-21T15:33:24.616338Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2512.24497"},"observation_digest":"sha256:6fa3bee3752588ed0eea0af8d433c47774efc4a4f5b6bc0fcbbf7bcacd2d6d06","observation_id":"4c0a3303-c3cc-41c3-8201-68831b42c261","resolution":{"observed_at":"2026-05-21T15:34:15.010899Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2601.02018","last_updated":"2026-04-27T02:00:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-01-05T11:28:58Z","title":"Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-05-16T18:11:47.141366Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.02018"},"observation_digest":"sha256:9202422152efcf6de2d5904020e9f710786785f8f295962f029b23015e8de8f9","observation_id":"bca09ef9-e511-4ebc-adb0-e311176a51a4","resolution":{"observed_at":"2026-05-16T18:13:13.153258Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-04T06:36:47.448156Z","title":"Di- nov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.02457","last_updated":"2026-08-02T08:24:45Z","snapshot_observed_at":"2026-08-06T12:12:56.989615Z","submitted_at":"2026-01-05T18:55:45Z","title":"PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T06:36:47.448156Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.02457"},"observation_digest":"sha256:a1e36e8c3dd9ac65ba1bd975f568c027c0576f822ce13fea89aafc71a7dfd781","observation_id":"bef07bcb-2def-414a-991c-018c6fa28a35","resolution":{"observed_at":"2026-08-04T06:36:47.448156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T12:26:40.101386Z","title":"Sim ´eoni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.03112","last_updated":"2026-06-18T07:06:52Z","snapshot_observed_at":"2026-08-03T12:26:32.791566Z","submitted_at":"2026-01-06T15:42:45Z","title":"DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T12:26:40.101386Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.03112"},"observation_digest":"sha256:c1aa6e9c7dc703718ea5139cea4871d2f726ac2a589a7bea68320937daf00659","observation_id":"c29bb0d1-0435-47b3-a0bd-9a9c5a67934e","resolution":{"observed_at":"2026-08-03T12:26:40.101386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2601.12964","last_updated":"2026-05-02T09:19:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-01-19T11:21:19Z","title":"Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T13:16:29.137187Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.12964"},"observation_digest":"sha256:3b786fee792f77b4426fe4960ad55bb28f3e7f57e4fec734df1d0afcbe580522","observation_id":"d152726d-620b-4897-b735-fbf8247f8bc2","resolution":{"observed_at":"2026-05-16T13:17:54.986097Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2601.14671","last_updated":"2026-04-13T06:15:42Z","snapshot_observed_at":"2026-07-06T22:42:26.083572Z","submitted_at":"2026-01-21T05:33:23Z","title":"Mirai: Autoregressive Visual Generation Needs Foresight","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-16T12:16:16.461488Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.14671"},"observation_digest":"sha256:da0de9384f939ecfdb407ccbe714553515a142d2854c097228e3f24eaad5e7b4","observation_id":"01cfa052-4f17-4c60-9e3f-9f72f4bf3d65","resolution":{"observed_at":"2026-05-16T12:17:52.082557Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T09:02:49.202458Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.15275","last_updated":"2026-07-06T18:52:05Z","snapshot_observed_at":"2026-08-03T09:02:45.848359Z","submitted_at":"2026-01-21T18:55:51Z","title":"RayRoPE: Projective Ray Positional Encoding for Multi-view Attention","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T09:02:49.202458Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.15275"},"observation_digest":"sha256:f1abd44320688d59e5e77f98e4bd74a878730cad0f2f5c4eabdc979d44a30964","observation_id":"d7febf5e-ce5a-46b8-bda4-57f503a47a35","resolution":{"observed_at":"2026-08-03T09:02:49.202458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T08:13:44.241700Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.17950","last_updated":"2026-07-19T01:16:30Z","snapshot_observed_at":"2026-08-05T15:19:52.247413Z","submitted_at":"2026-01-25T18:59:45Z","title":"UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T08:13:44.241700Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.17950"},"observation_digest":"sha256:dba1df8945743e2b65c49f5745bd86536d1354e1c531b4bda590c7197401ca45","observation_id":"826a8c7d-4e84-4393-8a75-a7a3228fc8b9","resolution":{"observed_at":"2026-08-03T08:13:44.241700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T07:37:46.236062Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.19618","last_updated":"2026-07-22T19:51:14Z","snapshot_observed_at":"2026-08-06T10:09:03.324643Z","submitted_at":"2026-01-27T13:50:43Z","title":"The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T07:37:46.236062Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.19618"},"observation_digest":"sha256:61114532d5760fd04282f46928028ed1c37559a6f11d689cd50c2545b1a6182c","observation_id":"fe2d11e2-aa9d-4042-bf3a-2cc5aa9a0d6e","resolution":{"observed_at":"2026-08-03T07:37:46.236062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2601.20524","last_updated":"2026-04-09T12:38:57Z","snapshot_observed_at":"2026-08-06T07:48:46.184595Z","submitted_at":"2026-01-28T12:02:58Z","title":"AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-16T10:47:17.480722Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.20524"},"observation_digest":"sha256:acd4b4d96cc46d7cabc4482855f70e3bb54e63250f2c7107a015cb3ae3186f46","observation_id":"ea0ce8ba-2d8a-47f4-b0d1-9cbb093fca9f","resolution":{"observed_at":"2026-05-16T10:47:45.459816Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T06:50:24.779427Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.22054","last_updated":"2026-07-03T08:31:57Z","snapshot_observed_at":"2026-08-04T05:57:33.604836Z","submitted_at":"2026-01-29T17:52:41Z","title":"MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-03T06:50:24.779427Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2601.22054"},"observation_digest":"sha256:6ab8cf2d3d9dcc1ef15a604b425aeeb05820f28a048733fa2885600edbed7b3c","observation_id":"51e8d30c-4c30-4189-9c3d-0d338512ed80","resolution":{"observed_at":"2026-08-03T06:50:24.779427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T05:44:12.034823Z","title":"Vincent, P., Larochelle, H., Bengio, Y ., and Manzagol, P.-A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.01391","last_updated":"2026-06-18T16:49:52Z","snapshot_observed_at":"2026-08-03T05:44:10.354793Z","submitted_at":"2026-02-01T18:51:12Z","title":"Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T05:44:12.034823Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.01391"},"observation_digest":"sha256:1db3be37b7d9cac30b2e4e8cc2e01dce5e4742af3ee0eba05d1f5c6f00acd84a","observation_id":"57cce296-036f-4d54-8312-31ae09027a35","resolution":{"observed_at":"2026-08-03T05:44:12.034823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.01738","last_updated":"2026-04-15T07:37:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-02-02T07:20:02Z","title":"Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-16T08:40:09.385451Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.01738"},"observation_digest":"sha256:7ada694eef6c8f6120cb9c870a3a47174ce3039861be621c480693db33ae6fdb","observation_id":"61c92240-ff23-43c6-ac9a-b2eb2a022e32","resolution":{"observed_at":"2026-05-16T08:40:46.248899Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T05:32:09.513188Z","title":"& Hein, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.02124","last_updated":"2026-06-30T01:14:45Z","snapshot_observed_at":"2026-08-06T11:51:27.887140Z","submitted_at":"2026-02-02T14:07:33Z","title":"Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomalies","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T05:32:09.513188Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.02124"},"observation_digest":"sha256:294704b09dd1aeca7e30ac66c354e153a880de3259dd64435ae6a0488a845558","observation_id":"af13348a-839e-4a08-a6d9-5ad0701e0312","resolution":{"observed_at":"2026-08-03T05:32:09.513188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.03139","last_updated":"2026-05-19T12:34:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-02-03T05:45:25Z","title":"Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-21T14:37:09.487669Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.03139"},"observation_digest":"sha256:a64c2bb82938a676fa503e3fd28c0f2a1c58a0bef5b64d60ce123fc6aa053b07","observation_id":"b2edd37d-8966-4e1c-a74b-1d6c9f4af436","resolution":{"observed_at":"2026-05-21T14:40:14.532646Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.05638","last_updated":"2026-04-17T08:31:14Z","snapshot_observed_at":"2026-08-04T22:30:41.280585Z","submitted_at":"2026-02-05T13:18:33Z","title":"SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-16T07:16:29.588452Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.05638"},"observation_digest":"sha256:a22cb36a0c477a2e8d8bec3c811bc292e7274bf4b1c0c6108e8ef28790d85cc4","observation_id":"1021f588-4279-4938-b861-5e2df567ecaf","resolution":{"observed_at":"2026-05-16T07:17:30.314610Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T04:08:18.513267Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.05845","last_updated":"2026-06-08T11:16:24Z","snapshot_observed_at":"2026-08-03T12:54:34.865192Z","submitted_at":"2026-02-05T16:33:30Z","title":"Self-Supervised Learning with a Multi-Task Latent Space Objective","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T04:08:18.513267Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.05845"},"observation_digest":"sha256:33fbfc6ef904614c7a0e66fd8d23c88195232a979a60a40ad3e9c251a334c121","observation_id":"45d74fc1-e692-4531-9bf6-9e0d09e40b28","resolution":{"observed_at":"2026-08-03T04:08:18.513267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.06912","last_updated":"2026-04-08T22:13:53Z","snapshot_observed_at":"2026-08-06T06:47:56.958894Z","submitted_at":"2026-02-06T18:07:20Z","title":"PANC: Prior-Aware Normalized Cut via Anchor-Augmented Token Graphs","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-16T06:41:17.449437Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.06912"},"observation_digest":"sha256:523fe2180774bf864237052a75253276e961513e3b693fd25ad4bde380ab573f","observation_id":"e9cc85df-1af1-4f93-b7ca-c43f1144e5e5","resolution":{"observed_at":"2026-05-16T06:42:27.814490Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T03:42:05.400340Z","title":"V ., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V ., Szafraniec, M., Yi, S., Ramamonjisoa, M., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.07429","last_updated":"2026-06-16T04:57:59Z","snapshot_observed_at":"2026-08-03T22:52:09.224964Z","submitted_at":"2026-02-07T08:00:47Z","title":"Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T03:42:05.400340Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.07429"},"observation_digest":"sha256:375597c0fef009b958a02181394199ee2804d5618282dcb0678cce3cdf9b338d","observation_id":"64f21cdd-2499-4ad0-aecf-62c8f7b2d042","resolution":{"observed_at":"2026-08-03T03:42:05.400340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-03T00:02:14.849577Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.11934","last_updated":"2026-06-08T09:01:34Z","snapshot_observed_at":"2026-08-04T13:02:28.723589Z","submitted_at":"2026-02-12T13:30:24Z","title":"Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T00:02:14.849577Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.11934"},"observation_digest":"sha256:9c26cc8b507d74875d8019b6a0ceaf0902597f43d8f86c3c5d8c12dd4af0e071","observation_id":"2b5282fd-cedd-431e-a6bc-58381023636a","resolution":{"observed_at":"2026-08-03T00:02:14.849577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T23:59:17.124942Z","title":"Song, J., Meng, C., and Ermon, S","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.12155","last_updated":"2026-06-29T06:08:55Z","snapshot_observed_at":"2026-08-04T12:11:40.185011Z","submitted_at":"2026-02-12T16:36:33Z","title":"FAIL: Flow Matching Adversarial Imitation Learning for Image Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T23:59:17.124942Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.12155"},"observation_digest":"sha256:7462e9eb6b0a7dddac390efb798e8292ed5d3e7b74b29739af72e8ada0cc822b","observation_id":"e2167caf-095e-4f7b-bd71-915bfd384348","resolution":{"observed_at":"2026-08-02T23:59:17.124942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T23:57:44.675360Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.12215","last_updated":"2026-06-03T04:04:20Z","snapshot_observed_at":"2026-08-02T23:57:43.860710Z","submitted_at":"2026-02-12T17:53:51Z","title":"LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T23:57:44.675360Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.12215"},"observation_digest":"sha256:a7d1ee8a5a5ae0fa1e7fd08229c9302e58531a9d9d1e80fd75a3209bacc4c374","observation_id":"54dc58ce-6d6a-4f11-a59f-46bc602cabb2","resolution":{"observed_at":"2026-08-02T23:57:44.675360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T22:25:59.691930Z","title":"Mannat Singh, Laura Gustafson, Aaron B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.16918","last_updated":"2026-07-13T23:32:16Z","snapshot_observed_at":"2026-08-02T22:25:56.850881Z","submitted_at":"2026-02-18T22:22:44Z","title":"Xray-Visual Models: Scaling Vision models on Industry Scale Data","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T22:25:59.691930Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.16918"},"observation_digest":"sha256:6ce17126b148b14881dc5e6ca7f2d91d7dd432fe16e6bec93632f6f4948538fd","observation_id":"a4cc25f9-992f-4ba6-9599-89d16fb03e88","resolution":{"observed_at":"2026-08-02T22:25:59.691930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T21:56:35.956566Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.18803","last_updated":"2026-07-02T13:59:22Z","snapshot_observed_at":"2026-08-06T11:31:56.604158Z","submitted_at":"2026-02-21T11:33:00Z","title":"Learning to Localize Reference Trajectories in Image-Space for Visual Navigation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T21:56:35.956566Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.18803"},"observation_digest":"sha256:6d03366f1a909c5f18165b296c69d647bc0e86072825b60396a6b31147a8a045","observation_id":"f006d4b0-1ead-4599-8cc4-0ed51e3e01b5","resolution":{"observed_at":"2026-08-02T21:56:35.956566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T21:47:27.543396Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.19213","last_updated":"2026-06-05T08:10:24Z","snapshot_observed_at":"2026-08-04T19:08:59.167123Z","submitted_at":"2026-02-22T14:48:42Z","title":"SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T21:47:27.543396Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.19213"},"observation_digest":"sha256:1a258cbfd4f5efca7e39ccd7bfe191d22068463147541c407b81f0e209d03a8d","observation_id":"32d63a07-3782-4dd6-ae28-e3cc24b39b90","resolution":{"observed_at":"2026-08-02T21:47:27.543396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T21:36:06.425621Z","title":"Dinov3.arXiv preprint arXiv:2508.10104, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.19863","last_updated":"2026-07-21T07:37:55Z","snapshot_observed_at":"2026-08-06T04:44:27.278410Z","submitted_at":"2026-02-23T14:09:01Z","title":"Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-02T21:36:06.425621Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.19863"},"observation_digest":"sha256:997b8d2c24fa091cdb6acff2d9e4edf69ec78d0a53d2bbe1e7ed21a698b03653","observation_id":"201fdea5-2628-4538-856e-6ee6f001249e","resolution":{"observed_at":"2026-08-02T21:36:06.425621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.20223","last_updated":"2026-04-09T07:40:01Z","snapshot_observed_at":"2026-07-06T22:46:49.536946Z","submitted_at":"2026-02-23T13:37:44Z","title":"MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-15T20:11:22.146641Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.20223"},"observation_digest":"sha256:244bde5e62532a3ea3458c49125d074263b19120f71ba14967ab30c4b1464938","observation_id":"17498c73-ff66-4375-83db-7ec4081a9124","resolution":{"observed_at":"2026-05-15T20:11:34.272487Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.23013","last_updated":"2026-05-13T09:15:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-02-26T13:52:57Z","title":"SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-15T18:48:08.212050Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.23013"},"observation_digest":"sha256:9bd918918f8f1b355e2ad331041f76bcaa95a9297110d6a7e5650dece7f168b7","observation_id":"41aeb9b8-c3fb-4927-bdf1-1369a9e06810","resolution":{"observed_at":"2026-05-15T18:50:16.775488Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T20:27:16.942403Z","title":"arXiv preprint arXiv:2508.10104 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.23297","last_updated":"2026-07-04T00:47:22Z","snapshot_observed_at":"2026-08-02T20:27:16.161614Z","submitted_at":"2026-02-26T18:07:52Z","title":"PRIMA: Pre-training with Risk-integrated Image-Metadata Alignment for Medical Diagnosis via LLM","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T20:27:16.942403Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.23297"},"observation_digest":"sha256:284dc3b8724305ef41cef9229aa98cec288c6fe5567290e7fb706ecb2e6f4ab9","observation_id":"323c7d98-0c37-4159-8ac3-8caf63d2c8a6","resolution":{"observed_at":"2026-08-02T20:27:16.942403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T20:27:15.665278Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.23353","last_updated":"2026-06-29T15:37:39Z","snapshot_observed_at":"2026-08-02T20:27:13.657906Z","submitted_at":"2026-02-26T18:55:06Z","title":"SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T20:27:15.665278Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.23353"},"observation_digest":"sha256:3d64b5003e9ffafc094a374a2e548cc0464d62fdd7ffec4bc4ca07ad75ecfb2d","observation_id":"d9d02977-36d3-4638-94af-0306b148792e","resolution":{"observed_at":"2026-08-02T20:27:15.665278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2602.24138","last_updated":"2026-05-20T06:04:15Z","snapshot_observed_at":"2026-07-06T22:47:19.784697Z","submitted_at":"2026-02-27T16:15:58Z","title":"Multimodal Optimal Transport for Training-free Temporal Segmentation in Surgical Robotics","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-21T11:38:01.845488Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2602.24138"},"observation_digest":"sha256:c0f778d35296d70c8f97cd999343662299372941ec828cb183f8cd9af5505872","observation_id":"36919d1c-4681-4f4f-b0e9-fb11f5a256b1","resolution":{"observed_at":"2026-05-21T11:40:03.376835Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":"2508.10104","doi":"10.1055/a-2487-1252","metadata_source":"pith","pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINOv3","venue":"cs.CV","work_id":"c8b07deb-8fe7-4e18-9620-f3569d3529ce","year":2025},"citing_paper":{"arxiv_id":"2603.01098","last_updated":"2026-04-17T17:58:14Z","snapshot_observed_at":"2026-07-06T22:47:28.681790Z","submitted_at":"2026-03-01T13:30:36Z","title":"Differential privacy representation geometry for medical image analysis","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T18:05:40.153547Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2603.01098"},"observation_digest":"sha256:5ada9ef8092e772bc5a227a4338f576b67b2ee3d4bd0fb7f1281216222cb5779","observation_id":"b75d8483-1bf4-4d40-8b04-0505d750cb14","resolution":{"observed_at":"2026-05-15T18:06:25.239050Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T19:45:29.468990Z","title":"Sim \\'e oni, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.01110","last_updated":"2026-07-30T11:25:17Z","snapshot_observed_at":"2026-08-06T06:13:34.890713Z","submitted_at":"2026-03-01T13:51:55Z","title":"Compact Task-Aligned Imitation Learning for Laboratory Automation","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-02T19:45:29.468990Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2603.01110"},"observation_digest":"sha256:74bdc43c179c2e332fbdef75a67dbece613b610ce34b6de8b7f6a657822f25b3","observation_id":"54ec6ce8-f183-4260-afb1-ff50dddd6bb6","resolution":{"observed_at":"2026-08-02T19:45:29.468990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T19:46:36.682107Z","title":"https://doi.org/10.48550/arXiv.2508.10104, https://ui.adsabs.harvard.edu/abs/2025arXiv250810104S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.01115","last_updated":"2026-07-10T14:42:18Z","snapshot_observed_at":"2026-08-02T19:46:32.819912Z","submitted_at":"2026-03-01T13:55:34Z","title":"LUMOS: Latent Universal Medical Priors for Segmentation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T19:46:36.682107Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2603.01115"},"observation_digest":"sha256:420543438f6013f0a1a57981f6f31ae23c6e85949530ca26a1cb77b528d573d8","observation_id":"376a34b1-e4fb-41d4-9556-10c6378ee4bd","resolution":{"observed_at":"2026-08-02T19:46:36.682107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-02T19:39:17.016668Z","title":"cc/paper_files/paper/2022/file/ ec795aeadae0b7d230fa35cbaf04c041-Paper-Conference","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.01544","last_updated":"2026-07-29T09:54:56Z","snapshot_observed_at":"2026-08-06T10:13:24.535459Z","submitted_at":"2026-03-02T07:15:37Z","title":"RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T19:39:17.016668Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2603.01544"},"observation_digest":"sha256:eaf7360b9bd9aeaa62eb52aa14074a27dee986d175f99527e3af11c8c8ab01ff","observation_id":"3a22770f-d492-4842-9138-4bcf95c148a2","resolution":{"observed_at":"2026-08-02T19:39:17.016668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.10104/citation-record","integrity":"/paper/2508.10104/integrity","json":"/paper/2508.10104/citation-record.json","paper":"/paper/2508.10104"},"outbound":[],"paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T22:12:35.584339Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 100 inbound Pith citation observations for arXiv:2508.10104."}