{"as_of":"2026-08-07T23:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2867418137c66c22b2ef7703e653df2c81890692abd48ca41d6d48fcec2c72d8","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:05:19.488311Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T20:38:42.563481Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T03:56:35.127847Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"cited_work":{"arxiv_id":"2507.00049","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.00049","snapshot_observed_at":"2026-07-02T03:56:35.127847Z","title":"Adadedup: Adaptive hybrid data pruning for efficient large-scale object detection training","venue":null,"work_id":"019f0f19-7c3a-4ed3-8349-ca53f88e848e","year":2026},"citing_paper":{"arxiv_id":"2604.08366","last_updated":"2026-04-09T15:33:00Z","snapshot_observed_at":"2026-07-06T22:57:26.678728Z","submitted_at":"2026-04-09T15:33:00Z","title":"Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T17:22:25.943097Z"},"links":{"cited_paper":"/paper/2507.00049","citing_paper":"/paper/2604.08366"},"observation_digest":"sha256:f9caa63855f4aea6668eb29aced3a2947986148d8d2190ac1b762fdcfd6f2197","observation_id":"6fbfa801-1566-43a2-9f2a-7f2c1d1f8e0e","resolution":{"observed_at":"2026-05-11T06:56:02.288684Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"cited_work":{"arxiv_id":"2507.00049","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.00049","snapshot_observed_at":"2026-07-02T03:56:35.127847Z","title":"Adadedup: Adaptive hybrid data pruning for efficient large-scale object detection training","venue":null,"work_id":"019f0f19-7c3a-4ed3-8349-ca53f88e848e","year":2026},"citing_paper":{"arxiv_id":"2606.04261","last_updated":"2026-06-02T22:26:53Z","snapshot_observed_at":"2026-08-02T20:22:03.575430Z","submitted_at":"2026-06-02T22:26:53Z","title":"Can Generalist Agents Automate Data Curation?","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T09:32:39.361415Z"},"links":{"cited_paper":"/paper/2507.00049","citing_paper":"/paper/2606.04261"},"observation_digest":"sha256:93c2468e0c888c4d9823e1682580bf95f17785e088101ffc3810c5149ac6c14f","observation_id":"9600728b-38be-4d23-bf75-7f62b737a7ab","resolution":{"observed_at":"2026-07-02T03:56:35.129131Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.00049","snapshot_observed_at":"2026-08-01T20:38:42.563481Z","title":"Adadedup: Adaptive hybrid data pruning for efficient large-scale object detection training.arXiv preprint arXiv:2507.00049, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22697","last_updated":"2026-07-18T00:28:57Z","snapshot_observed_at":"2026-08-07T05:51:07.395695Z","submitted_at":"2026-07-18T00:28:57Z","title":"Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T20:38:42.563481Z"},"links":{"cited_paper":"/paper/2507.00049","citing_paper":"/paper/2607.22697"},"observation_digest":"sha256:ea77e939f61f336749eae5eea1061e0f1a3baef5dd50823a1ac21f8bf7f68088","observation_id":"1021dff6-719b-430f-94b0-369afe2392bc","resolution":{"observed_at":"2026-08-01T20:38:42.563481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.00049/citation-record","integrity":"/paper/2507.00049/integrity","json":"/paper/2507.00049/citation-record.json","paper":"/paper/2507.00049"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:27.874750Z","title":"How much more data do i need? estimating requirements for downstream tasks","venue":null,"work_id":"650868ac-9e86-42a3-892c-059c5b51bc26","year":2022},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.314050Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:e71fef0349b5c1df6ed30728002c19a9e6d6111e904dc22edbdd6848037c1067","observation_id":"df092bff-07a4-4e0e-8887-30e7e22d9577","resolution":{"observed_at":"2026-08-06T23:05:28.014744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.09540","last_updated":"2023-03-22T17:22:35Z","snapshot_observed_at":"2026-08-06T15:07:40.203199Z","submitted_at":"2023-03-16T17:53:24Z","title":"SemDeDup: Data-efficient learning at web-scale through semantic deduplication","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.09540","snapshot_observed_at":"2026-08-06T23:05:16.343098Z","title":"Semd- edup: Data-efficient learning at web-scale through semantic deduplication.arXiv preprint arXiv:2303.09540, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.343098Z"},"links":{"cited_paper":"/paper/2303.09540","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:1f5713a7ff85843c152f1396e981c1a6737bada0d6788015da43fe3a09cb1bba","observation_id":"543d5c5b-99ce-47dc-98e0-2fd57955c782","resolution":{"observed_at":"2026-08-06T23:05:16.343098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:27.469997Z","title":"Optimizing data collection for machine learning.Journal of Machine Learning Research, 26(38):1–52, 2025","venue":null,"work_id":"808c51b5-f9d9-47f7-9aea-f82bcd2050be","year":2025},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.363422Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:41b7ca9d61069aa4b59827e21e97743d985742c4ddec762c95d2716df8d8e74e","observation_id":"801f729f-da63-416f-9923-ed6f3eed2863","resolution":{"observed_at":"2026-08-06T23:05:27.644835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:16.375997Z","title":"Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.375997Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:465f5928178130041fc5039496ff0291bc20393e198a1044d60b554e0ca119e5","observation_id":"bad166d6-0574-4981-8d6f-b702dcddcf9d","resolution":{"observed_at":"2026-08-06T23:05:16.375997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:27.110248Z","title":"Sse: Multimodal semantic data selection and enrichment for industrial-scale data assimilation.ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025","venue":null,"work_id":"c8f3b712-24da-41b2-b004-a11e098eb05d","year":2025},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.383899Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:452a63cde65f955b4012f0de67dfcc8cfdfab410ffb5ecc70821739c0561504a","observation_id":"6054ab60-5b70-47e7-88ad-fc35592d390c","resolution":{"observed_at":"2026-08-06T23:05:27.209821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04578","last_updated":"2024-03-12T10:35:56Z","snapshot_observed_at":"2026-07-06T17:13:17.263599Z","submitted_at":"2024-01-09T14:32:24Z","title":"Effective pruning of web-scale datasets based on complexity of concept clusters","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04578","snapshot_observed_at":"2026-08-06T23:05:16.387914Z","title":"Effective pruning of web-scale datasets based on complexity of concept clusters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.387914Z"},"links":{"cited_paper":"/paper/2401.04578","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:0d02335439a7f3d490176dff3fa44fc36d8433ffbad51465c295a1ee3b934202","observation_id":"4b1ab1d7-d169-41db-98aa-fcad646ef059","resolution":{"observed_at":"2026-08-06T23:05:16.387914Z","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":"2026-08-06T23:05:16.393755Z","title":"Zero-shot coreset selection: Efficient pruning for unlabeled data.arXiv preprint arXiv:2411.15349, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.393755Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:0ecbeaf5e058b3e8586418b5a78d6fe5810643245f236286e9b3c97c91732d5f","observation_id":"63247bde-863f-4f3f-915c-4fdd2c7fd10d","resolution":{"observed_at":"2026-08-06T23:05:16.393755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:26.784764Z","title":"Efficient coreset selection with cluster-based methods","venue":null,"work_id":"edabef59-5c7c-417b-bd7b-644cfc667ec9","year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.397315Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:689d761521cbabdfb8e0f2beaacfd68f7b272153c76a84af77fed23790302451","observation_id":"75cd7295-e410-4728-b7e8-e083be2f0a3b","resolution":{"observed_at":"2026-08-06T23:05:26.924748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:26.354763Z","title":"Moderate coreset: A universal method of data selection for real-world data-efficient deep learning","venue":null,"work_id":"9f5b7a30-c814-4480-b791-e0e9b10f503d","year":2022},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.434750Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:8a7222ede55e7b96eb57c241c6875276ab96e0460c511aae88323d1cc2c0eda8","observation_id":"30b93b8f-956e-491a-9731-551f0a7e365f","resolution":{"observed_at":"2026-08-06T23:05:26.640880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:25.544878Z","title":"Data pruning via moving-one-sample-out.Advances in neural information processing systems, 36: 18251–18262, 2023","venue":null,"work_id":"0710040f-8dc1-47cf-90dd-ba5c5633022a","year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.485078Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:2783a85dc032a52f85a7ace7960e20247d704033872c96e1e6d73d65396db3f7","observation_id":"27b66ebc-d49f-49e8-b514-da280b265ef1","resolution":{"observed_at":"2026-08-06T23:05:25.684759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:24.994748Z","title":"Data curation via joint example selection further accelerates multimodal learning.Advances in Neural Information Processing Systems, 37:141240–141260, 2024","venue":null,"work_id":"c585146c-ac70-4b43-9c26-f1ffcb568b4e","year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.513565Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:d4f18cb241cfd378e6a728872e8a6659e0157f60f7fe95ed5a95864999486457","observation_id":"81a77012-a96a-4efe-bbab-3930ff5d4fc9","resolution":{"observed_at":"2026-08-06T23:05:25.225174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.11829","last_updated":"2020-10-27T00:52:20Z","snapshot_observed_at":"2026-07-06T08:03:24.054624Z","submitted_at":"2019-06-26T23:01:47Z","title":"Selection via Proxy: Efficient Data Selection for Deep Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.11829","snapshot_observed_at":"2026-08-06T23:05:16.571968Z","title":"Selection via proxy: Efficient data selection for deep learning.arXiv preprint arXiv:1906.11829, 2019","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.571968Z"},"links":{"cited_paper":"/paper/1906.11829","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:2202cf6fbd4d8ef3596999a6c0a58a321d525cdb01faca61e04d3d0fd3561714","observation_id":"79239bdb-65d4-42e0-b0f1-ce1ff346bc1a","resolution":{"observed_at":"2026-08-06T23:05:16.571968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:24.494432Z","title":"Coreset selection for object detection","venue":null,"work_id":"b01f3339-50fe-4207-b1a7-41483af1cc8d","year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.600515Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:351def46b337da8c853476f16ac2a15e77b787f28c39228eea181b55554a8437","observation_id":"eacc3155-12d0-43aa-97e1-d0b54709299e","resolution":{"observed_at":"2026-08-06T23:05:24.687139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:23.960113Z","title":"Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data.Proceedings of the ACM on Management of Data, 1(2):1–27, 2023","venue":null,"work_id":"23d916c7-68a8-4250-be15-06e99fb2e7c1","year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.655093Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:e10c0f6a773d27d9d94015139ef27338dd7d22b0fed9041eefece7b236165215","observation_id":"3fe57e05-7a8b-48a5-972d-f3078253aeab","resolution":{"observed_at":"2026-08-06T23:05:24.276016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:16.692484Z","title":"nuscenes: A multimodal dataset for autonomous driving","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.692484Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:daf11901b6fdfc0263d9afeb840c9709a9a70412d673a0f3a5b1cd04950a6f9d","observation_id":"3da021c2-a114-4376-bf05-ae1dd5893103","resolution":{"observed_at":"2026-08-06T23:05:16.692484Z","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":"2026-08-06T23:05:16.705078Z","title":"Scalability in perception for autonomous driving: Waymo open dataset","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.705078Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:a2df53af1a142aca1008a3835ff53d2bd901e3173e40fc701b45acafc59774f3","observation_id":"0675a12f-453e-4d67-9dd2-397902a8f84e","resolution":{"observed_at":"2026-08-06T23:05:16.705078Z","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":"2026-08-06T23:05:16.720049Z","title":"Lvis: A dataset for large vocabulary instance segmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.720049Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:2e5ab44091267b757042de93a5b97fad7b04d19ff2cc4abbdb2f6d57bbaeeac5","observation_id":"277c114e-3a35-47a1-a70a-6dcbf3c18dc7","resolution":{"observed_at":"2026-08-06T23:05:16.720049Z","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":"2026-08-06T23:05:16.844745Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.844745Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:9c4854b93f6dc93fdff74a3d44936857e575682e087f4413b95208adc8c19a36","observation_id":"743eae11-e7fd-41c5-aa34-192f7807026b","resolution":{"observed_at":"2026-08-06T23:05:16.844745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:23.408828Z","title":"Performance scaling via optimal transport: Enabling data selection from partially revealed sources.Advances in Neural Information Processing Systems, 36:61341–61363, 2023","venue":null,"work_id":"55527b8f-028c-431d-9078-10578bcfa422","year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:16.959664Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:8892a1a1f643462fe5fc83dc513262f7c75cd6dcf6e0239a245a98404432f289","observation_id":"61881223-4809-447a-8288-85a17946b662","resolution":{"observed_at":"2026-08-06T23:05:23.564580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:17.099478Z","title":"Understanding black-box predictions via influence functions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.099478Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:cb19d3935884490149fc1594e980d779290f5865193a3f82780ab85a2f440341","observation_id":"3124ad71-1d6c-467e-a46e-75af66985240","resolution":{"observed_at":"2026-08-06T23:05:17.099478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00489","last_updated":"2018-06-01T10:17:23Z","snapshot_observed_at":"2026-07-06T05:53:39.440274Z","submitted_at":"2017-08-01T19:50:53Z","title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00489","snapshot_observed_at":"2026-08-06T23:05:17.215945Z","title":"Active learning for convolutional neural networks: A core-set approach.arXiv preprint arXiv:1708.00489, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.215945Z"},"links":{"cited_paper":"/paper/1708.00489","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:2680ae968f53cf8cb16325e2f393e5a69bf9af9e8abebf6c879eb927cb923330","observation_id":"48ef057a-a115-485e-92f5-3b9b82afad2a","resolution":{"observed_at":"2026-08-06T23:05:17.215945Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05159","last_updated":"2019-11-15T17:08:30Z","snapshot_observed_at":"2026-08-01T06:33:25.731787Z","submitted_at":"2018-12-12T21:24:15Z","title":"An Empirical Study of Example Forgetting during Deep Neural Network Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05159","snapshot_observed_at":"2026-08-06T23:05:17.373329Z","title":"An empirical study of example forgetting during deep neural network learning.arXiv preprint arXiv:1812.05159, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.373329Z"},"links":{"cited_paper":"/paper/1812.05159","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:317bcb6df300c21b242b19bd7bf2353fcfa5cf24245e1967e5b4ebde1eecdeba","observation_id":"ec9f6b0e-b343-47e2-9233-e88e2c9dff33","resolution":{"observed_at":"2026-08-06T23:05:17.373329Z","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":"2026-08-06T23:05:17.494757Z","title":"Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.494757Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:019aca3d1616183cf48ccb9ce9a8aeb41f8b67873c00c62cf8c0fa3a45356242","observation_id":"c3d4775e-8c76-47d0-badf-6cf2cfcc8cde","resolution":{"observed_at":"2026-08-06T23:05:17.494757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:23.064748Z","title":"Coresets via bilevel optimization for continual learning and streaming.Advances in neural information processing systems, 33: 14879–14890, 2020","venue":null,"work_id":"9ef6de68-e6ac-4a8f-886a-9ec37c237613","year":2020},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.634760Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:816be3e81589540d654961500f4b1512df70f67cc0d3e927a70cfb0a8f3d1199","observation_id":"db496b88-e096-4b81-98d2-37a61dfccc95","resolution":{"observed_at":"2026-08-06T23:05:23.188679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.11719","last_updated":"2023-07-09T21:53:45Z","snapshot_observed_at":"2026-08-07T21:40:40.556424Z","submitted_at":"2022-07-24T11:23:31Z","title":"Gradient-based Bi-level Optimization for Deep Learning: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.11719","snapshot_observed_at":"2026-08-06T23:05:17.754757Z","title":"Gradient-based bi-level optimization for deep learning: A survey.arXiv preprint arXiv:2207.11719, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.754757Z"},"links":{"cited_paper":"/paper/2207.11719","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:c297adadd3d2afff9935ffafe94d718c2d7993d41ff3e32105405f58e1891ee5","observation_id":"534cf64d-9d93-4bf7-84a8-5ef277a4b5ac","resolution":{"observed_at":"2026-08-06T23:05:17.754757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:22.674753Z","title":"Springer Science & Business Media, 1998","venue":null,"work_id":"ceaf31dc-5538-4d56-b9bf-adcf6e716323","year":1998},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.830474Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:50fc124a48092c2250ff150de3b625f0f521ce79ba31c899e8671bfc545a60d9","observation_id":"f87939b7-05ff-4bf8-8123-9da59246c5db","resolution":{"observed_at":"2026-08-06T23:05:22.834754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.00622","last_updated":"2022-02-01T18:15:24Z","snapshot_observed_at":"2026-08-05T23:13:05.033311Z","submitted_at":"2022-02-01T18:15:24Z","title":"Datamodels: Predicting Predictions from Training Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.00622","snapshot_observed_at":"2026-08-06T23:05:17.959429Z","title":"Datamodels: Predicting predictions from training data.arXiv preprint arXiv:2202.00622, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:17.959429Z"},"links":{"cited_paper":"/paper/2202.00622","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:1cb8527dc108f8c1d475315fa52d9d8e386707cce5f731e14cf1b6e0f8a6ce7e","observation_id":"9bde4033-9a53-4ca5-a709-fbf69d375687","resolution":{"observed_at":"2026-08-06T23:05:17.959429Z","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":"2026-08-06T23:05:18.055970Z","title":"Autoscale: Automatic prediction of compute-optimal data composition for training llms.arXiv preprint arXiv:2407.20177, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.055970Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:76173c0a2147861a3680cd870a06e3c3a6a31f01473201e6d5c54bc0f720e633","observation_id":"954c19a8-77e9-43a9-b6ba-fd903efb4431","resolution":{"observed_at":"2026-08-06T23:05:18.055970Z","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":"2026-08-06T23:05:18.281909Z","title":"Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.281909Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:cb671a594156f71aabfff656fef89290a5f1779882f0741752295e10bbecbc51","observation_id":"d57657e4-056d-4efd-b467-745183bff5d8","resolution":{"observed_at":"2026-08-06T23:05:18.281909Z","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":"2026-08-06T23:05:18.414756Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.414756Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:fd39b6d5846816e36f4445e2eb7070e228419bb2c6e3a3d4dafa81bf982f0a6e","observation_id":"bd12d4f9-5682-438a-b892-7c0e059b4ec1","resolution":{"observed_at":"2026-08-06T23:05:18.414756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:22.114750Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016","venue":null,"work_id":"fb87d495-9994-4959-96dc-8091435a2c77","year":2016},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.503139Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:4e6d49f7cba2beefbf99c2b13a1a8efe24766d3df2c3b673a9946da2c3d63cf6","observation_id":"d6f60cdb-6113-4d21-af70-02c342b744f0","resolution":{"observed_at":"2026-08-06T23:05:22.254852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:18.611387Z","title":"Detectron2","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.611387Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:eb9a9c45d1288351d598790e7ba231a44d716204a1680d44dc79bd84d9638250","observation_id":"fa675ba9-778c-44f6-85df-891fa30eda39","resolution":{"observed_at":"2026-08-06T23:05:18.611387Z","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":"2026-08-06T23:05:18.658590Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.658590Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:cd7a2e9c1e81df615ae9b20a7f78f7a887ad7a0d6cf2bbab3e3761ece8e39981","observation_id":"73ae992f-e6b4-49e0-8023-74e342996359","resolution":{"observed_at":"2026-08-06T23:05:18.658590Z","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":"2026-08-06T23:05:18.734412Z","title":"Grounding dino: Marrying dino with grounded pre-training for open-set object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.734412Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:248dbedfbb5c827cae2f51a86ccde7333333b3bf17833838f43af57e3d7cb447","observation_id":"3e7326f9-8050-45c3-94b3-28c37fecf0bc","resolution":{"observed_at":"2026-08-06T23:05:18.734412Z","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":"2026-08-06T23:05:18.791413Z","title":"Improved baselines with visual instruction tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.791413Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:b4ee8c517bca48b5373fc11553d3fde4820fcd737cc0219946ecda209b108045","observation_id":"9c55ee1f-b522-4b07-b89a-a853b7b30a93","resolution":{"observed_at":"2026-08-06T23:05:18.791413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04272","last_updated":"2023-09-26T04:43:31Z","snapshot_observed_at":"2026-07-06T14:40:12.494082Z","submitted_at":"2023-01-11T02:25:10Z","title":"Data Distillation: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04272","snapshot_observed_at":"2026-08-06T23:05:18.887426Z","title":"Data distillation: A survey.arXiv preprint arXiv:2301.04272, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.887426Z"},"links":{"cited_paper":"/paper/2301.04272","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:ff80e7b3bf8a5ac9c543138322f892f8bd62abacabf89c5066da71357c044a01","observation_id":"8f1a53f7-f1b1-4688-9ee9-8813fedbc8ca","resolution":{"observed_at":"2026-08-06T23:05:18.887426Z","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":"2026-08-06T23:05:18.988068Z","title":"Glister: Generalization based data subset selection for efficient and robust learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:18.988068Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:cd6e62ef38d1687d58e6c92c087b00af5ae85c6539b04f68e15036f93228c935","observation_id":"3892ec02-ade5-4c19-81ca-e4d17933f0f3","resolution":{"observed_at":"2026-08-06T23:05:18.988068Z","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":"2026-08-06T23:05:19.154962Z","title":"Grad-match: Gradient matching based data subset selection for efficient deep model training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:19.154962Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:604bd42f107116a8b4f70a6dbcdf1b92cdef4c4d48c0cd76bca84ba43d7f1d7f","observation_id":"eb772b09-e0cb-4486-80d6-9180cfcea93f","resolution":{"observed_at":"2026-08-06T23:05:19.154962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.00054","last_updated":"2023-12-19T20:14:51Z","snapshot_observed_at":"2026-07-06T15:21:23.714725Z","submitted_at":"2023-04-28T19:05:16Z","title":"LAVA: Data Valuation without Pre-Specified Learning Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.00054","snapshot_observed_at":"2026-08-06T23:05:19.248709Z","title":"Lava: Data valuation without pre-specified learning algorithms.arXiv preprint arXiv:2305.00054, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:19.248709Z"},"links":{"cited_paper":"/paper/2305.00054","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:209de86b1060290d009eae85e129a507da9826cdf92491ffbd1e92638987ff3f","observation_id":"138d5e7b-863f-4c4a-8f16-d86e7fe89b68","resolution":{"observed_at":"2026-08-06T23:05:19.248709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:05:21.324829Z","title":"Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33: 19920–19930, 2020","venue":null,"work_id":"c7960e9c-9651-42e7-89d4-155c4b569e9a","year":2020},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:19.346626Z"},"links":{"citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:f5559e545c7f664ca0e3e72ebd81c75ba418f487b171032c1561f8e135dcb034","observation_id":"b97147c0-475f-4324-9591-13ad768c0f23","resolution":{"observed_at":"2026-08-06T23:05:21.414754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02774","last_updated":"2024-05-05T00:08:00Z","snapshot_observed_at":"2026-07-06T18:09:55.249620Z","submitted_at":"2024-05-05T00:08:00Z","title":"Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs","version":1},"cited_work":{"arxiv_id":"2405.02774","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.02774","snapshot_observed_at":"2026-08-06T23:05:19.685320Z","title":"Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs","venue":"cs.LG","work_id":"d5ee6d7b-3113-4b46-b372-211b4d032ed7","year":2024},"citing_paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:05:19.488311Z"},"links":{"cited_paper":"/paper/2405.02774","citing_paper":"/paper/2507.00049"},"observation_digest":"sha256:e04cced8c27f99a1f64da833c69189594d4ea288b9ff168ba23cf3e9b56dad6d","observation_id":"b98c8d11-f9cd-47c0-8857-ca48f2a0bca2","resolution":{"observed_at":"2026-08-06T23:05:19.774905Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.00049","last_updated":"2025-06-24T22:35:51Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T22:55:40.355679Z","submitted_at":"2025-06-24T22:35:51Z","title":"AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":1,"verified_fuzzy":14},"total_outbound_references":41},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2507.00049."}