{"as_of":"2026-08-13T09:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:db9ac8a4eb19e0530dedf10422381f2146fea8c8c30714e9b6a2183cf947cbf0","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T13:34:55.310461Z","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":"2026-05-25T07:45:29.677524Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-11T13:34:55.310461Z","title":"Yolov6 v3","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13006","last_updated":"2024-12-17T15:26:15Z","snapshot_observed_at":"2026-08-13T06:00:44.756865Z","submitted_at":"2024-12-17T15:26:15Z","title":"What is YOLOv6? A Deep Insight into the Object Detection Model","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T13:34:55.310461Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2412.13006"},"observation_digest":"sha256:3ea2d39ac70b766122fbfb2b2dd59fd5f4ed5af51be64d2c37095fe20e48799d","observation_id":"5dece9e4-f277-4019-ac0d-66b0789481ab","resolution":{"observed_at":"2026-08-11T13:34:55.310461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-10T20:25:43.119733Z","title":"YOLOv6 v3.0: A full-scale reloading,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.08639","last_updated":"2025-01-15T08:04:44Z","snapshot_observed_at":"2026-08-13T07:14:05.771535Z","submitted_at":"2025-01-15T08:04:44Z","title":"Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T20:25:43.119733Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2501.08639"},"observation_digest":"sha256:cc127bbcaf3648b5abba6fb733d65c49c3933400b6f33ea54fb4717afbe3f895","observation_id":"7d53046a-da2b-4834-ab30-a152e256e193","resolution":{"observed_at":"2026-08-10T20:25:43.119733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-08T22:02:52.283998Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04656","last_updated":"2025-02-26T03:02:16Z","snapshot_observed_at":"2026-08-11T10:17:19.404516Z","submitted_at":"2025-02-07T04:49:42Z","title":"MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T22:02:52.283998Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2502.04656"},"observation_digest":"sha256:1b85309394f9076da65837a323730f8db2b21db3d670a76b1936be165fb2ca9a","observation_id":"1a03c9ee-4a21-461e-9c15-a9d266bcd537","resolution":{"observed_at":"2026-08-08T22:02:52.283998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":"2301.05586","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Yolov6 v3.0: A full-scale reloading","venue":null,"work_id":"701760e9-1367-46c1-9250-17122c3d47b6","year":2023},"citing_paper":{"arxiv_id":"2502.12524","last_updated":"2025-02-18T04:20:14Z","snapshot_observed_at":"2026-07-06T20:38:20.545914Z","submitted_at":"2025-02-18T04:20:14Z","title":"YOLOv12: Attention-Centric Real-Time Object Detectors","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-13T21:35:11.147146Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2502.12524"},"observation_digest":"sha256:7527c32c88faaf153e7e552a7704ca1da93ed439fbf953ea56989fe97b4ebfac","observation_id":"f1efcbc3-8c80-452e-a022-085550cb0e17","resolution":{"observed_at":"2026-05-13T21:35:11.193662Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-07T13:53:12.040362Z","title":"Yolov6 v3.0: A full-scale reloading","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20687","last_updated":"2025-05-27T03:58:50Z","snapshot_observed_at":"2026-08-09T21:34:40.749739Z","submitted_at":"2025-05-27T03:58:50Z","title":"VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:53:12.040362Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2505.20687"},"observation_digest":"sha256:aa9f6cfe7f6bd8b99ecf70894ad9498394f89f22fc7373f88da35ab19076a04c","observation_id":"4cf98524-1f0d-48dc-b364-13df3e59c6dc","resolution":{"observed_at":"2026-08-07T13:53:12.040362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-07T10:18:27.700080Z","title":"Yolov6 v3","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05806","last_updated":"2025-06-06T07:09:07Z","snapshot_observed_at":"2026-08-07T20:50:54.524255Z","submitted_at":"2025-06-06T07:09:07Z","title":"LLIA -- Enabling Low-Latency Interactive Avatars: Real-Time Audio-Driven Portrait Video Generation with Diffusion Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:27.700080Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2506.05806"},"observation_digest":"sha256:b111bd7cd236b4c066e21e562508a45cc63accd4319987a5e0768e549c040212","observation_id":"548058e6-ad0b-41b6-8251-ab0ed6dd69a3","resolution":{"observed_at":"2026-08-07T10:18:27.700080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-06T17:45:26.397371Z","title":"Yolov6 v3. 0: A full-scale reloading,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10006","last_updated":"2025-07-14T07:39:55Z","snapshot_observed_at":"2026-08-11T08:39:18.612415Z","submitted_at":"2025-07-14T07:39:55Z","title":"Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-06T17:45:26.397371Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2507.10006"},"observation_digest":"sha256:ae981948afc567fa08aab9e3ddb3edbd575d64be48cad75688e8bc8c695f9d92","observation_id":"f84060be-1a38-4244-a7ba-782f3c09cf7f","resolution":{"observed_at":"2026-08-06T17:45:26.397371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":"2301.05586","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Yolov6 v3.0: A full-scale reloading","venue":null,"work_id":"701760e9-1367-46c1-9250-17122c3d47b6","year":2023},"citing_paper":{"arxiv_id":"2512.07078","last_updated":"2026-05-22T17:42:16Z","snapshot_observed_at":"2026-08-13T07:18:29.655806Z","submitted_at":"2025-12-08T01:25:10Z","title":"DFIR-DETR: Frequency-Domain Iterative Refinement and Dynamic Feature Aggregation for Small Object Detection","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-25T07:42:10.040344Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2512.07078"},"observation_digest":"sha256:32e1692259d5a2eeef305688b58cdc9a82f1f3a9e062104d0b5b2e632203fd57","observation_id":"9cb037e0-79c3-4201-b771-bb9c21e4a42c","resolution":{"observed_at":"2026-05-25T07:45:29.680649Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":"2301.05586","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Yolov6 v3.0: A full-scale reloading","venue":null,"work_id":"701760e9-1367-46c1-9250-17122c3d47b6","year":2023},"citing_paper":{"arxiv_id":"2604.19233","last_updated":"2026-04-21T08:36:51Z","snapshot_observed_at":"2026-08-13T07:15:43.241321Z","submitted_at":"2026-04-21T08:36:51Z","title":"Adaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T02:15:57.389751Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2604.19233"},"observation_digest":"sha256:e3bf63cd2543d0959c3171f7754b86fa3337d11d9045f0b781603bc1295f93e5","observation_id":"539eb711-f8b1-48b8-9155-731e90385694","resolution":{"observed_at":"2026-05-11T13:11:04.377677Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-07-11T21:31:04.773453Z","title":"Yolov6 v3. 0: A full-scale reloading,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.04125","last_updated":"2026-07-05T05:42:56Z","snapshot_observed_at":"2026-08-13T07:01:32.665063Z","submitted_at":"2026-07-05T05:42:56Z","title":"FRFDet: Efficient UAV Small Object Detection with Symmetric Sampling and Scalable Fusion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-11T21:31:04.773453Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2607.04125"},"observation_digest":"sha256:db172578c1aea60bca3403401054199dae82d1df34968f7f7912e9a3dcf2c6c7","observation_id":"a95e5480-2927-43f4-b378-d3e857607636","resolution":{"observed_at":"2026-07-11T21:31:04.773453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05586","snapshot_observed_at":"2026-08-06T18:10:14.375718Z","title":"Yolov6 v3","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.04720","last_updated":"2026-08-07T05:16:40Z","snapshot_observed_at":"2026-08-12T23:10:11.070590Z","submitted_at":"2026-08-05T11:39:06Z","title":"YOLOv14: Adaptive Real-Time Object Detection for Diverse Imaging Conditions","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T18:10:14.375718Z"},"links":{"cited_paper":"/paper/2301.05586","citing_paper":"/paper/2608.04720"},"observation_digest":"sha256:57d994337eae87ee1aa1b61d2dc7b10b43fba13d687340b630c652d4dd9c0917","observation_id":"a82a09a0-ccb9-430a-8940-e110e68994fe","resolution":{"observed_at":"2026-08-06T18:10:14.375718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2301.05586/citation-record","integrity":"/paper/2301.05586/integrity","json":"/paper/2301.05586/citation-record.json","paper":"/paper/2301.05586"},"outbound":[],"paper":{"arxiv_id":"2301.05586","last_updated":"2023-01-13T14:46:46Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T04:18:34.791809Z","submitted_at":"2023-01-13T14:46:46Z","title":"YOLOv6 v3.0: A Full-Scale Reloading"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2301.05586."}