{"as_of":"2026-08-15T13:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:352ba5974e0885d57f6305681ae1b204989384b1fad06a5f65fcc3533423b3b4","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":"2026-08-15T06:32:42.880941+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":"2026-08-10T21:15:54.453209Z","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":"2026-08-07T00:58:48.370863Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.11448","last_updated":"2023-03-20T20:49:08Z","snapshot_observed_at":"2026-08-13T12:20:24.176477Z","submitted_at":"2023-03-20T20:49:08Z","title":"Geometrical aspects of lattice gauge equivariant convolutional neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11448","snapshot_observed_at":"2026-08-10T21:15:54.453209Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.05580","last_updated":"2025-01-09T21:14:25Z","snapshot_observed_at":"2026-08-11T03:26:26.156370Z","submitted_at":"2025-01-09T21:14:25Z","title":"Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T21:15:54.453209Z"},"links":{"cited_paper":"/paper/2303.11448","citing_paper":"/paper/2501.05580"},"observation_digest":"sha256:7e443dd1094653452a80431448623c37d805cb30c40d639fe2eda84e8075c9d8","observation_id":"c65168e2-943f-4d57-ac19-4c8dd53e2a20","resolution":{"observed_at":"2026-08-10T21:15:54.453209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11448","last_updated":"2023-03-20T20:49:08Z","snapshot_observed_at":"2026-08-13T12:20:24.176477Z","submitted_at":"2023-03-20T20:49:08Z","title":"Geometrical aspects of lattice gauge equivariant convolutional neural networks","version":1},"cited_work":{"arxiv_id":"2303.11448","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.11448","snapshot_observed_at":"2026-08-07T00:58:48.370863Z","title":"Geometrical aspects of lattice gauge equivariant convolutional neural networks","venue":"hep-lat","work_id":"a063e503-04a8-4465-a807-093c0bd81c46","year":2023},"citing_paper":{"arxiv_id":"2506.12493","last_updated":"2025-06-14T13:12:25Z","snapshot_observed_at":"2026-08-07T17:48:45.226951Z","submitted_at":"2025-06-14T13:12:25Z","title":"Symmetry-preserving neural networks in lattice field theories","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T00:58:38.860519Z"},"links":{"cited_paper":"/paper/2303.11448","citing_paper":"/paper/2506.12493"},"observation_digest":"sha256:df08fee753bd7899f6befd4032ecef2ec0762128209bccc23e0554ad2406e809","observation_id":"fe9fcd2d-e8b2-4b80-b215-6e34b64cd9bf","resolution":{"observed_at":"2026-08-07T00:58:48.433396Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2303.11448/citation-record","integrity":"/paper/2303.11448/integrity","json":"/paper/2303.11448/citation-record.json","paper":"/paper/2303.11448"},"outbound":[],"paper":{"arxiv_id":"2303.11448","last_updated":"2023-03-20T20:49:08Z","latest_version":1,"primary_category":"hep-lat","snapshot_observed_at":"2026-08-13T12:20:24.176477Z","submitted_at":"2023-03-20T20:49:08Z","title":"Geometrical aspects of lattice gauge equivariant convolutional neural networks"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.11448."}