{"as_of":"2026-08-09T12:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ad1cbc8ab50590190d91fe9b87a431cdc98af6ba4e64e503160dfd32c1b1af9a","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-04T08:36:52.302337Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-03T03:57:38.843200Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1708.00684","last_updated":"2017-08-02T10:20:22Z","snapshot_observed_at":"2026-07-06T05:53:44.547337Z","submitted_at":"2017-08-02T10:20:22Z","title":"OmniArt: Multi-task Deep Learning for Artistic Data Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00684","snapshot_observed_at":"2026-08-04T08:36:52.302337Z","title":"OmniArt: Multi-Task Deep Learning for Artistic Data Analysis,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.19986","last_updated":"2025-10-22T19:34:19Z","snapshot_observed_at":"2026-08-04T08:36:49.192683Z","submitted_at":"2025-10-22T19:34:19Z","title":"Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T08:36:52.302337Z"},"links":{"cited_paper":"/paper/1708.00684","citing_paper":"/paper/2510.19986"},"observation_digest":"sha256:f71192e7ee807901d1f6d9abad74bb32425d88448ada8693fe095666e9ead21c","observation_id":"b5418837-48a8-4c1c-b4c7-5c3e95529e99","resolution":{"observed_at":"2026-08-04T08:36:52.302337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00684","last_updated":"2017-08-02T10:20:22Z","snapshot_observed_at":"2026-07-06T05:53:44.547337Z","submitted_at":"2017-08-02T10:20:22Z","title":"OmniArt: Multi-task Deep Learning for Artistic Data Analysis","version":1},"cited_work":{"arxiv_id":"1708.00684","doi":null,"metadata_source":"pith","pith_arxiv_id":"1708.00684","snapshot_observed_at":"2026-07-03T03:57:38.843200Z","title":"OmniArt: Multi-task Deep Learning for Artistic Data Analysis","venue":"cs.MM","work_id":"a371cd29-2c96-426f-a6fb-c02b169ee5be","year":2017},"citing_paper":{"arxiv_id":"2606.09648","last_updated":"2026-06-08T15:40:57Z","snapshot_observed_at":"2026-08-09T12:01:25.649338Z","submitted_at":"2026-06-08T15:40:57Z","title":"ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-27T14:19:07.591742Z"},"links":{"cited_paper":"/paper/1708.00684","citing_paper":"/paper/2606.09648"},"observation_digest":"sha256:7cbe60982e4f0a625341f6d01c558e28eed6cfe121e9aa003136c7dd08dfabac","observation_id":"8be37458-4a07-4258-980d-b1d06d245dab","resolution":{"observed_at":"2026-07-03T03:57:38.844385Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00684","last_updated":"2017-08-02T10:20:22Z","snapshot_observed_at":"2026-07-06T05:53:44.547337Z","submitted_at":"2017-08-02T10:20:22Z","title":"OmniArt: Multi-task Deep Learning for Artistic Data Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00684","snapshot_observed_at":"2026-08-02T04:43:48.487947Z","title":"arXiv preprint arXiv:1708.00684 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.16321","last_updated":"2026-07-15T09:28:48Z","snapshot_observed_at":"2026-08-07T03:27:35.528117Z","submitted_at":"2026-07-15T09:28:48Z","title":"Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T04:43:48.487947Z"},"links":{"cited_paper":"/paper/1708.00684","citing_paper":"/paper/2607.16321"},"observation_digest":"sha256:84ca6ab30a22e8e771dad047aece3f45e22c27635987b6d69cacfc06d65a2be4","observation_id":"c9650b38-f29c-4929-b933-c9e8c9ce4baf","resolution":{"observed_at":"2026-08-02T04:43:48.487947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1708.00684/citation-record","integrity":"/paper/1708.00684/integrity","json":"/paper/1708.00684/citation-record.json","paper":"/paper/1708.00684"},"outbound":[],"paper":{"arxiv_id":"1708.00684","last_updated":"2017-08-02T10:20:22Z","latest_version":1,"primary_category":"cs.MM","snapshot_observed_at":"2026-07-06T05:53:44.547337Z","submitted_at":"2017-08-02T10:20:22Z","title":"OmniArt: Multi-task Deep Learning for Artistic Data Analysis"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1708.00684."}