{"as_of":"2026-08-07T20:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5e898fc153fc0e9de2d6dea79a4d8af0e21caff4dc9d1e4ad79ed0ff2f6b1fbc","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:45:52.929510Z","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":"2026-08-06T19:44:34.489375Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.07323","last_updated":"2025-02-11T07:42:44Z","snapshot_observed_at":"2026-08-06T03:36:14.984453Z","submitted_at":"2025-02-11T07:42:44Z","title":"Semantic to Structure: Learning Structural Representations for Infringement Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07323","snapshot_observed_at":"2026-08-06T19:45:52.929510Z","title":"Semantic to structure: Learning structural representations for infringe- ment detection.arXiv preprint arXiv:2502.07323, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.04699","last_updated":"2025-07-07T06:47:10Z","snapshot_observed_at":"2026-08-06T19:39:31.849674Z","submitted_at":"2025-07-07T06:47:10Z","title":"A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:45:52.929510Z"},"links":{"cited_paper":"/paper/2502.07323","citing_paper":"/paper/2507.04699"},"observation_digest":"sha256:df59a78a658901a13e940d02c8537f98058ca0ddc961952106fe85ce4f31899a","observation_id":"784193e0-7fa7-4c41-970e-390885a53cf6","resolution":{"observed_at":"2026-08-06T19:45:52.929510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07323","last_updated":"2025-02-11T07:42:44Z","snapshot_observed_at":"2026-08-06T03:36:14.984453Z","submitted_at":"2025-02-11T07:42:44Z","title":"Semantic to Structure: Learning Structural Representations for Infringement Detection","version":1},"cited_work":{"arxiv_id":"2502.07323","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.07323","snapshot_observed_at":"2026-08-06T19:44:34.489375Z","title":"Semantic to Structure: Learning Structural Representations for Infringement Detection","venue":"cs.CV","work_id":"2129b15e-e959-4558-b78f-f855c60464ca","year":2025},"citing_paper":{"arxiv_id":"2507.04769","last_updated":"2025-07-07T08:45:08Z","snapshot_observed_at":"2026-08-06T19:37:40.836858Z","submitted_at":"2025-07-07T08:45:08Z","title":"From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:44:32.012666Z"},"links":{"cited_paper":"/paper/2502.07323","citing_paper":"/paper/2507.04769"},"observation_digest":"sha256:054f0f29d055cb7c7d807d64a804eb53f0847f8e18d06e98a567f0f03a0a121c","observation_id":"0f9fe709-2441-40cf-9941-fd1a7e13a52f","resolution":{"observed_at":"2026-08-06T19:44:34.520092Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2502.07323/citation-record","integrity":"/paper/2502.07323/integrity","json":"/paper/2502.07323/citation-record.json","paper":"/paper/2502.07323"},"outbound":[],"paper":{"arxiv_id":"2502.07323","last_updated":"2025-02-11T07:42:44Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T03:36:14.984453Z","submitted_at":"2025-02-11T07:42:44Z","title":"Semantic to Structure: Learning Structural Representations for Infringement Detection"},"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":"2026-08-07T06:34:17.273281+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 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.07323."}