{"as_of":"2026-08-05T23:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67282f4496045c68d6f430a3c9c27d0755bcd9375c01ef962c1070a7e711995a","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T19:09:46.332644Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2606.00782/citation-record","integrity":"/paper/2606.00782/integrity","json":"/paper/2606.00782/citation-record.json","paper":"/paper/2606.00782"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2512.16771","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:12:34.636458Z","title":"Flowdet: Unifying object detection and generative transport flows.arXiv preprint arXiv:2512.16771, 2025","venue":null,"work_id":"be608d14-ba5f-43b2-8403-d1a759a01c15","year":2025},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:dca4354fa3d2985265373c7544a0f8e45e9ab359045afb627872a1389094751e","observation_id":"6f73bcb5-7ca1-42d7-9826-94dc3d658bb6","resolution":{"observed_at":"2026-06-28T19:12:34.638428Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:09:46.332644Z","title":"End-to-end object detection with transformers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:4e2b868fe1f364d1706e22a23a81aadc8bd168ca19010872245dc55ba4aa9e16","observation_id":"1c95c5cd-2445-4888-ba8f-651525e9a243","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Diffusiondet: Diffusion model for object detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:3bb67c529acba641f5f219f6fbbffb28833b70df8de038cf5a2ac8feae0ca897","observation_id":"67111ac1-a3c4-4661-a944-35a9871e28f2","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Yolo- world: Real-time open-vocabulary object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:16e8261551e39c75a50135f7353cfa3601117385e1c471fb24c266926c1fa13e","observation_id":"68de5e34-2224-41db-9739-ee636876ad58","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Dynamic head: Unifying object detection heads with attentions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:ecba49075f4d909668856b972d84bca102f9843928ac54ffe9d169ff296ed376","observation_id":"136e103f-000c-4cc2-8c09-e07fe400a84b","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:26d3326bcd825fab11123e947ee9d885907021d8ba387899fa9043fa825679da","observation_id":"a7c69dd5-29ca-48d6-bf19-398dd6139a69","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Generative adversarial nets.Advances in neural information processing systems, 27, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:5833cc3762c2c7e5e900d4719b38dfb0e5ac8dc18b7cd54db077598ff2b99ed0","observation_id":"d74a8772-4e1c-4c3f-bc9f-659f67ccaf4d","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Lvis: A dataset for large vocabulary instance segmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:d68cdce41a6ed72b92cce1668f6cae520cb95f88978f02bcdc18d7792f68c44c","observation_id":"fa88e147-3e50-4d23-bd3d-fcefbf2141a6","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Mask r-cnn","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:bb22a15178689eb09a9f4e4d39f9f21766218a112ac0fe4d53689ac9cf00dbc0","observation_id":"64c68771-288b-43a2-b3f7-130e9a3acf78","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:fb8fd8162adc330f23bc743d1b1f65ee4369ce3e741535ebe5b0d3f14ff87176","observation_id":"57422601-4302-4f4d-bcd5-0577cb62da3d","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Mdetr-modulated detection for end-to-end multi-modal understanding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:e854e1fbf25fcfe24de46935556445094cc95c596960d3abe56e412a9705e821","observation_id":"394d74d1-6b37-4da4-9707-25c509a79673","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Grounded language-image pre-training","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:3e118296264d0505c83f522783db89e1ea7a3c4849d5b77db482d85251d4a20d","observation_id":"b6f15045-9aab-4a61-b5fb-bddbb2582244","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:14ba017b7e99ac1f3f552c788fd52ea3359bd43c99958d8e4b29985ec04200f9","observation_id":"fb61668d-4a19-4f7c-93d1-159818dca7cd","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:a5ff6f04a0695f6fbdec77a3b77fbf973e548f5b545971020faf5ad4f2584415","observation_id":"04f7f8e1-0bf1-475a-ab8f-5c082ec481b1","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-02T18:24:58.914589Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":"2210.02747","doi":"10.1038/s41467-024-47656-z","metadata_source":"pith","pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flow Matching for Generative Modeling","venue":"cs.LG","work_id":"6edb71c4-5d64-40af-a394-9757ea051a36","year":2022},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:5de98c8319cd377cf9d0154f10c6ec7fd61fbe29f6b5a8f69ee8762e797ec579","observation_id":"1b7d4af4-f270-4118-a228-d84588e61a00","resolution":{"observed_at":"2026-06-28T19:12:34.643432Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-06-01T22:57:59.860918+00:00","source":"crossref_status_cache"},{"observed_at":"2026-06-01T22:57:59.860918+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-28T19:09:46.332644Z","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":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:da9e8aa25622a582d76a9d8a5407325a350ddd0ffeba839b7f9979854866b5b0","observation_id":"046de1ef-6428-4b65-8d6b-287603f87982","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":"2209.03003","doi":"10.48550/arxiv.2209.03003","metadata_source":"pith","pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","venue":"cs.LG","work_id":"a1989e1b-d66d-4533-be3a-fb9c5fd62290","year":2022},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:7b12aac128632adcee151de095489aa4e8d5eb23c72ed1d5c5320830d0b39724","observation_id":"9e1aee25-b7ae-45ff-a005-3e7edd4d391d","resolution":{"observed_at":"2026-06-28T19:12:34.640805Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T22:49:29.244251+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T22:49:29.244251+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-28T19:09:46.332644Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:2d0e3eef73d9ac57d4a4d5adec3f9a354ec9dba3190920f79e0fa03ed2e6c4fa","observation_id":"e4202012-6a22-47e1-b3d2-185f81e98193","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":"1711.05101","doi":"10.1137/1.9781611972825.47","metadata_source":"pith","pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Decoupled Weight Decay Regularization","venue":"cs.LG","work_id":"07ef7360-d385-4033-83f7-8384a6325204","year":2017},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:6ba52611daeb582ec1f0085bd985cec472ec62d339ab4a9f5032eb700ccaefd0","observation_id":"70a62ca5-cd5a-4e45-8adf-5ed16dd3ed6a","resolution":{"observed_at":"2026-06-28T19:12:34.645709Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:09:46.332644Z","title":"Dynamic-dino: Fine-grained mixture of experts tuning for real-time open-vocabulary object detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:8f2c4862cfcfdded4c2a7b6821b0d2de9d84875b022cb1bd8eb85c76ab764f00","observation_id":"e55c33ec-1114-4f0e-a9c7-cf623e63dc99","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Film: Visual reasoning with a general conditioning layer","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:35a21d7551d41a40c5094616287f034f87ce2e5c5d0ddbc4684f4ef68e5b1ab5","observation_id":"9d8560ee-6fb5-4388-b23a-54f186b6fc37","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"You only look once: Unified, real-time object detection","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:10e9af7da44b812b9ebcde123b122e196f6ab04ff8fff4b49b8f4cd2aa872031","observation_id":"a02490e5-a0b0-4d6d-8a1b-2e03d66afe49","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Yolo9000: better, faster, stronger","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:11d6b40574a4cee7816763145e8257ed0f6ed7b81a7d3c4e8064f3008cd2b955","observation_id":"6dd7b6a4-93bb-4fd6-8de9-fe76a98c0b24","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks.Advances in neural information processing systems, 28, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:5e29d72f5538686eaaa5267a185808f0730532c5d7f8b053d23207ca47afecf4","observation_id":"533b8f13-480e-4648-957c-9303a5cc2569","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2405.10300","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.10300","snapshot_observed_at":"2026-07-04T13:29:51.401689Z","title":"Grounding dino 1.5: Advance the” edge” of open-set object detection","venue":null,"work_id":"6a6347d8-e869-489f-9e0e-19ffb2023438","year":2024},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"cited_paper":"/paper/2405.10300","citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:f1658da5fa3a34d48c30ae8893f3adb748ae52ed615d5b864f91cf04a3905af0","observation_id":"ec001988-3cba-46e2-a8bc-d4025ce048c4","resolution":{"observed_at":"2026-06-28T19:12:34.632582Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:09:46.332644Z","title":"Generalized intersection over union: A metric and a loss for bounding box regression","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:3ea241b7981d3bc9814526fe74008ab999a0ef443ff49e667a7c3c4da8d3c198","observation_id":"c7fc04a3-b8e8-4da7-af30-4bdad2c48bdb","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Variational inference with normalizing flows","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:2bffb59382f0861ae1b6feec0fa5d038177b3be41743276e6d25527f3d779728","observation_id":"7c181046-2afd-4378-8368-e4252f8f8a73","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Objects365: A large-scale, high-quality dataset for object detection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:bfc82621c101d1209b7cf486656a4ba1895795afb87e4552f41ee870cb9bea77","observation_id":"e90a3c7d-6078-4276-8dee-d03ddf492d1f","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Benchmarking object detectors with coco: A new path forward","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:0fc27568913448ea67ebf4faca2611b4745750d7668e1eecd38c5057b3bfde5b","observation_id":"0bafbb5f-ec41-4fcb-84e4-7ab7be2aa08c","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2026-06-28T19:09:46.332644Z","title":"Attention is all you need.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:ef0bbf518f1151c4200bdf5026d02221cd81a39e276f48fcf5677f3ee4d60302","observation_id":"f3aaa62f-2b91-4c53-8f05-7158684c1b15","resolution":{"observed_at":"2026-06-28T19:09:46.332644Z","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":"2510.06139","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T00:07:42.278795Z","title":"Deforming videos to masks: Flow matching for referring video segmentation","venue":null,"work_id":"4f7a63d4-bfb7-46fa-b72f-f92458d9a85f","year":2025},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:540d9428bb5abfc943f6d13095ad3aae90ca613f8e69f32e7a201eb8debb73fe","observation_id":"d43623fa-111e-4e1a-8021-d9f1792a2b28","resolution":{"observed_at":"2026-06-28T19:12:34.635285Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.03605","last_updated":"2022-07-11T10:30:29Z","snapshot_observed_at":"2026-08-04T22:53:41.491205Z","submitted_at":"2022-03-07T18:55:26Z","title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","version":4},"cited_work":{"arxiv_id":"2203.03605","doi":"10.48550/arxiv.2203.03605","metadata_source":"pith","pith_arxiv_id":"2203.03605","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection","venue":"cs.CV","work_id":"4e842f69-fa99-4dde-b8a7-b5a558d4c80b","year":2022},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"cited_paper":"/paper/2203.03605","citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:60660a175ae74af80d46d4d7811c686980526cc0b616bcff84d6398b07839e0c","observation_id":"316c36e9-9c15-4d9c-8061-a642470e5559","resolution":{"observed_at":"2026-06-28T19:12:34.626612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04159","last_updated":"2021-03-18T03:14:26Z","snapshot_observed_at":"2026-07-06T10:02:45.105181Z","submitted_at":"2020-10-08T17:59:21Z","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","version":4},"cited_work":{"arxiv_id":"2010.04159","doi":"10.48550/arxiv.2010.04159","metadata_source":"pith","pith_arxiv_id":"2010.04159","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","venue":"cs.CV","work_id":"876f9fe8-c712-4550-9c26-cc18ab69abf2","year":2020},"citing_paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T19:09:46.332644Z"},"links":{"cited_paper":"/paper/2010.04159","citing_paper":"/paper/2606.00782"},"observation_digest":"sha256:2bea83411add5150434aa3e61ab3dae5323414cd51be7fa404e107066a308d3c","observation_id":"56366c60-3cc0-41e4-8a3e-1d3f6f61d70e","resolution":{"observed_at":"2026-06-28T19:12:34.629627Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.00782","last_updated":"2026-05-30T16:01:43Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T10:40:26.599180Z","submitted_at":"2026-05-30T16:01:43Z","title":"FlowOVD: Learning Generative Latent Flows for Zero-shot Open-vocabulary Detection"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":25,"verified_exact":7,"verified_fuzzy":0},"total_outbound_references":33},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2606.00782."}