{"as_of":"2026-08-08T07:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bb53b5e6896b3e083ee39b0fa33ad9811534710b14efd76d338409e335393175","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T19:25:54.403316Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.09297/citation-record","integrity":"/paper/2509.09297/integrity","json":"/paper/2509.09297/citation-record.json","paper":"/paper/2509.09297"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.244088Z","title":"Class semantics modulation for open-set instance segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.244088Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:02f151545cf7598f3cd39bc7b16fba1c91a6ca65aeff32a6edf5af778a3f80d6","observation_id":"ba0d73a5-326f-44ff-bf73-b0d39c1374d9","resolution":{"observed_at":"2026-08-04T19:25:54.244088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.249625Z","title":"Open-set object detection using classification-free object proposal and instance-level contrastive learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.249625Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:d779b199011f00233d02f91be76ed57d070a19d3f74c94c5e6c4bbdd37e687a8","observation_id":"baa9a690-6cfc-4b9b-8044-f5fe5626ab14","resolution":{"observed_at":"2026-08-04T19:25:54.249625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.254562Z","title":"Deep deterministic uncertainty: A new simple baseline,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.254562Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:906a886766812558f89113ecce2682ae7984428b177ec4d6adc896f8cdc190a6","observation_id":"2d4aa9d7-9f51-4cb2-97f8-b77f94f5f8f2","resolution":{"observed_at":"2026-08-04T19:25:54.254562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.259293Z","title":"Uncertainty for identifying open-set errors in visual object detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.259293Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:f1372db674a46f78a33a23c76bb4587963e122900e5d39193f527647b99e0b15","observation_id":"c8748bf9-d9dc-4c82-96f4-756ac9ab066b","resolution":{"observed_at":"2026-08-04T19:25:54.259293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.263987Z","title":"Common corruptions for evaluating and enhancing robustness in air-to-air visual object detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.263987Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:43b8ee2dfaa3c84e7f9b54c73d2370facfb0795fe3ed678244bb2ea12ebdb82d","observation_id":"c2543c96-6552-4635-a33e-4a03aa58af34","resolution":{"observed_at":"2026-08-04T19:25:54.263987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.269884Z","title":"Air-to-air visual detection of micro-uavs: An experimental evaluation of deep learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.269884Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:43215d03882a052dbf9d3d607fa2c832b40a3c2af80265f60edd4ea15f85c77c","observation_id":"eaf6c4bf-c895-4766-a8f3-6c3c028406f6","resolution":{"observed_at":"2026-08-04T19:25:54.269884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.275339Z","title":"Nefeli: A deep-learning detection and tracking pipeline for enhancing autonomy in advanced air mobility,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.275339Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:b3024b0cea262f237f8bce8e008db05de696bd50bedc36498d3f1f93e269d51e","observation_id":"82badf87-472f-4e25-bd4f-57b856e6c92e","resolution":{"observed_at":"2026-08-04T19:25:54.275339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.279935Z","title":"Airtrack: Onboard deep learning framework for long-range aircraft detection and tracking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.279935Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:5db6b2931fcc3d94f87c653f36c7463bf280f43bcb49fa9f7673192056bf1057","observation_id":"7e894680-88a4-4ad8-9102-25755034ec23","resolution":{"observed_at":"2026-08-04T19:25:54.279935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.284396Z","title":"Visual-based obstacle detection and tracking, and conflict detection for small uas sense and avoid,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.284396Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:20ad8af4178fdc9936b6b4c7cd39ad03c741cc22de2a82a7c30671e509ca1e00","observation_id":"32e6bd70-d395-4756-bdbf-0f410d0d55d9","resolution":{"observed_at":"2026-08-04T19:25:54.284396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.288880Z","title":"Out-of-distribution identification: Let detector tell which i am not sure,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.288880Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:e57f9850d7218368a3f039c5b51c458907ef54625152a73d0907c87ac7131c6a","observation_id":"d4b51f8c-982c-4c12-b317-3e47267cfa7b","resolution":{"observed_at":"2026-08-04T19:25:54.288880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.293493Z","title":"Hyperdimensional feature fusion for out-of-distribution detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.293493Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:33fd8ddcb4494eb3ce5a64b4e4c9914a148ae7ce28dc5ec47bb5156dd728bb18","observation_id":"d69ffaad-9049-4f98-91f7-fc23faff5964","resolution":{"observed_at":"2026-08-04T19:25:54.293493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.298051Z","title":"Open-set object detection: towards unified problem formulation and benchmarking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.298051Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:82d159cc269ca06060ed359b8b13979e36fd4a1b357d775e2ad9afcd33715706","observation_id":"7785ee74-38a5-4fa3-ac6a-269c643c7c1c","resolution":{"observed_at":"2026-08-04T19:25:54.298051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10300","last_updated":"2024-06-01T03:35:22Z","snapshot_observed_at":"2026-08-05T08:31:40.101811Z","submitted_at":"2024-05-16T17:54:15Z","title":"Grounding DINO 1.5: Advance the \"Edge\" of Open-Set Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10300","snapshot_observed_at":"2026-08-04T19:25:54.302542Z","title":"Grounding dino 1.5: Advance the","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.302542Z"},"links":{"cited_paper":"/paper/2405.10300","citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:d5586d155993c102d9fc3ed480d12d393aa40591d18a8ac7515f8e31713a72af","observation_id":"c53ce271-3cca-43df-a7c0-bafcaed4e58d","resolution":{"observed_at":"2026-08-04T19:25:54.302542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.307521Z","title":"Uncertainty quantification for safe and reliable autonomous vehicles: A review of methods and applications,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.307521Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:4e68c7380860e7fbc7ac6ee2c561d0ea0b806708b2a975b57ce8698eb9a938d9","observation_id":"9b0eb14f-37e9-4e61-b504-60145b09d4e4","resolution":{"observed_at":"2026-08-04T19:25:54.307521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.312113Z","title":"Gaussian mixture models,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.312113Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:a3a9e616ab1bf25587993e478218751b426bdd77180ef953dbc9a15dadf6f68a","observation_id":"c299d861-238a-4c1c-b6d9-7beda2951f45","resolution":{"observed_at":"2026-08-04T19:25:54.312113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.316434Z","title":"Certainnet: Sampling-free uncertainty estimation for object detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.316434Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:bf3eb612e2341d3ed6fc4c3ab014ab6581bf337540d1d1f9ca5b5cbee119db96","observation_id":"5d85d5d8-eda6-45f4-8fd9-11ae8f325dcf","resolution":{"observed_at":"2026-08-04T19:25:54.316434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.321041Z","title":"Spectral normal- ization for generative adversarial networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.321041Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:72fe71c9ce4993d3c0117368d8562125378845c98acb56d42baf02505f0d3e35","observation_id":"345c8d29-c33e-47e7-bcfd-379a8521ae1d","resolution":{"observed_at":"2026-08-04T19:25:54.321041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.325674Z","title":"On calibration of modern neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.325674Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:515f230aed62ff8e1bde9a957ae1c49a3bf6bf7510a9ed6cceac4baeef60237f","observation_id":"9f9758e4-4d36-4856-8813-7ac5be41f9a5","resolution":{"observed_at":"2026-08-04T19:25:54.325674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.330140Z","title":"Benchmarking neural network ro- bustness to common corruptions and perturbations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.330140Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:0d1c825f3811e6d70b89e52bcfd6b9b99c40829b12ec69feb54268cc49f44f9c","observation_id":"4fc72332-01fd-442f-a368-df7dc0bb33b8","resolution":{"observed_at":"2026-08-04T19:25:54.330140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.334625Z","title":"The cityscapes dataset for semantic urban scene understanding,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.334625Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:e16accfdcab8da316d301f58a917af88df8d91aedfc44759844ef012c4b085b2","observation_id":"0a1dbdfd-6a91-489b-9d74-a02e4c25ceae","resolution":{"observed_at":"2026-08-04T19:25:54.334625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.339179Z","title":"nuscenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.339179Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:7210494e3c2559c849ea00368868306698106889d70ba2f73e608427541a4361","observation_id":"ff71d368-9d18-499b-96c2-f6303330f2db","resolution":{"observed_at":"2026-08-04T19:25:54.339179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.344880Z","title":"ultralytics/yolov5: v3.1 - bug fixes and performance improvements,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.344880Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:53221dfb0f67b252538154991b0ec99d720dbc83483f3d3db61afe997be4444d","observation_id":"620c9645-ef6e-4d4b-bc61-86b95410efb5","resolution":{"observed_at":"2026-08-04T19:25:54.344880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.350197Z","title":"Ultralytics yolo,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.350197Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:600948babff44a176f8672827cb4ace1a6b734ea69faa1804280a459001faeeb","observation_id":"b35bb4b9-9f66-4a1e-8675-1af15ada7671","resolution":{"observed_at":"2026-08-04T19:25:54.350197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-08-04T19:25:54.354669Z","title":"Yolox: Exceeding yolo series in 2021,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.354669Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:eb7dabb0543473a4aa8248d096b08e6f3759d606ebecfb82e952a7516e066518","observation_id":"d2ea0cf9-cc55-466d-992e-2690de4a9aa0","resolution":{"observed_at":"2026-08-04T19:25:54.354669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.359340Z","title":"Focal loss for dense object detection,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.359340Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:071e5ba1bce8498a128b26ba37e3d3a2da30727d29b22888653bc08ac2efbb16","observation_id":"9f08e96b-db1b-4bb7-8a8a-993a904a1b2f","resolution":{"observed_at":"2026-08-04T19:25:54.359340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.365172Z","title":"Pytorch-retinanet: Pytorch implementation of retinanet,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.365172Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:8a411fbc16c32dba3f655317c72c76a3d4d5d857bc40efd28f33a7da445b3999","observation_id":"f95de5fa-7859-4143-bd39-d03aee0921cb","resolution":{"observed_at":"2026-08-04T19:25:54.365172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.375473Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.375473Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:7bfeaa1bf09b5711e2955136565ddf7a22471d8fe1bf3e4f8babb803e3c843ab","observation_id":"e02660f0-f2fe-49b7-9d6a-131021242f47","resolution":{"observed_at":"2026-08-04T19:25:54.375473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.379953Z","title":"Faster r-cnn pytorch training pipeline,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.379953Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:d92e8a14c99da5708a3aa78eb9ec9219209362e391909f843c6a30f2d9d71c51","observation_id":"8a381af8-94cd-47ef-ba03-790241737bdd","resolution":{"observed_at":"2026-08-04T19:25:54.379953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.384697Z","title":"Diffusiondet: Diffusion model for object detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.384697Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:bcd363746923c4b7ba95c8e6061dd83ed221fbdedcbee9aaa473887f8da95831","observation_id":"933ff4a9-82e2-48af-9dd6-268aecd1da92","resolution":{"observed_at":"2026-08-04T19:25:54.384697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.389292Z","title":"End-to-end object detection with transformers,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.389292Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:f53b4614fc725546e260bbfc5a1512291340a38d65dc44ca09d985628df00bc3","observation_id":"a17cfeda-798b-433a-8561-706badb4ec8f","resolution":{"observed_at":"2026-08-04T19:25:54.389292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07461","last_updated":"2021-03-12T18:56:17Z","snapshot_observed_at":"2026-08-02T19:12:44.969249Z","submitted_at":"2021-03-12T18:56:17Z","title":"Probabilistic two-stage detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.07461","snapshot_observed_at":"2026-08-04T19:25:54.394192Z","title":"Probabilistic two-stage detec- tion,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.394192Z"},"links":{"cited_paper":"/paper/2103.07461","citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:9365e798fb362934ccc199caedb3f4470e5ee6ce247b72bf08d98742e9f29124","observation_id":"3e0ad0dc-ce87-4e2d-b879-4e597e9eb1d8","resolution":{"observed_at":"2026-08-04T19:25:54.394192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.399047Z","title":"DETRs Beat YOLOs on Real-time Object Detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.399047Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:6eac7559daa79b2bb873561d8d09a2baa169b209949b35607f2577bc6f5af477","observation_id":"0e0bde89-92e6-41ff-a695-d98e523894f7","resolution":{"observed_at":"2026-08-04T19:25:54.399047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.403316Z","title":"Airborne object tracking dataset,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.403316Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:b4f7021bb849c7717729ba01d7762e8f74ea50c5187c9cd89a5ca5a969726858","observation_id":"a228b0ce-6eda-4d25-9482-ae00b94d89e3","resolution":{"observed_at":"2026-08-04T19:25:54.403316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T19:25:54.369743Z","title":"Available: https://github.com/yhenon/pytorchretinanet","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-04T19:25:54.369743Z"},"links":{"citing_paper":"/paper/2509.09297"},"observation_digest":"sha256:877cc3e708436d4acdd6bb567cf23bf16715a1e9438b1f7ab0528eafa60b65ad","observation_id":"75ee9a98-e013-44ac-9b75-03b9cb37cad0","resolution":{"observed_at":"2026-08-04T19:25:54.369743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.09297","last_updated":"2025-09-11T09:40:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T19:25:53.935565Z","submitted_at":"2025-09-11T09:40:06Z","title":"Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":34},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2509.09297."}