{"as_of":"2026-08-09T14:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8d09fdbf7621520721bd86934b388fcecb48ba654fefd1ddf0a5b0b6f0400be","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":"2026-08-09T11:38:41.354041Z","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":"2026-05-19T22:27:49.548899Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1810.12879","last_updated":"2019-03-15T09:18:40Z","snapshot_observed_at":"2026-08-07T20:12:57.045299Z","submitted_at":"2018-10-30T17:22:57Z","title":"Regressive and generative neural networks for scalar field theory","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.12879","snapshot_observed_at":"2026-08-09T11:38:41.354041Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.02670","last_updated":"2025-02-20T21:20:33Z","snapshot_observed_at":"2026-08-09T14:09:52.357796Z","submitted_at":"2025-02-04T19:18:44Z","title":"Machine-learning approaches to accelerating lattice simulations","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T11:38:41.354041Z"},"links":{"cited_paper":"/paper/1810.12879","citing_paper":"/paper/2502.02670"},"observation_digest":"sha256:09b552538a29adbf7811a5bf3c40db3b3f06ef0ccad539304789552cb9e8823d","observation_id":"538f2bdb-c26e-44a8-9338-a90bd029e8fd","resolution":{"observed_at":"2026-08-09T11:38:41.354041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.12879","last_updated":"2019-03-15T09:18:40Z","snapshot_observed_at":"2026-08-07T20:12:57.045299Z","submitted_at":"2018-10-30T17:22:57Z","title":"Regressive and generative neural networks for scalar field theory","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.12879","snapshot_observed_at":"2026-08-07T00:58:31.820940Z","title":null,"venue":null,"work_id":null,"year":2019},"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":26,"source":"pdf_text","source_observed_at":"2026-08-07T00:58:31.820940Z"},"links":{"cited_paper":"/paper/1810.12879","citing_paper":"/paper/2506.12493"},"observation_digest":"sha256:f3b21c994471948c1390008a8395c8263a4323274c68e5a1e1bc59b643d24ea7","observation_id":"0cabe103-4889-4c11-ba78-6b9bbcc28047","resolution":{"observed_at":"2026-08-07T00:58:31.820940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.12879","last_updated":"2019-03-15T09:18:40Z","snapshot_observed_at":"2026-08-07T20:12:57.045299Z","submitted_at":"2018-10-30T17:22:57Z","title":"Regressive and generative neural networks for scalar field theory","version":3},"cited_work":{"arxiv_id":"1810.12879","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.12879","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Regressive and generative neural networks for scalar field theory","venue":"hep-lat","work_id":"5a9c67f7-5e7b-41a1-b32d-7c1327667b69","year":2018},"citing_paper":{"arxiv_id":"2605.17511","last_updated":"2026-07-12T19:25:46Z","snapshot_observed_at":"2026-08-01T04:07:46.363373Z","submitted_at":"2026-05-17T15:46:39Z","title":"Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-19T22:24:51.990184Z"},"links":{"cited_paper":"/paper/1810.12879","citing_paper":"/paper/2605.17511"},"observation_digest":"sha256:4fa6fc2b5029d3cff4f6d3e628c3010e56f3cb1828444806b5add7d1bb282f32","observation_id":"c14e52e4-8d69-45ac-aa02-6ba5b48e2024","resolution":{"observed_at":"2026-05-19T22:27:49.550295Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/1810.12879/citation-record","integrity":"/paper/1810.12879/integrity","json":"/paper/1810.12879/citation-record.json","paper":"/paper/1810.12879"},"outbound":[],"paper":{"arxiv_id":"1810.12879","last_updated":"2019-03-15T09:18:40Z","latest_version":3,"primary_category":"hep-lat","snapshot_observed_at":"2026-08-07T20:12:57.045299Z","submitted_at":"2018-10-30T17:22:57Z","title":"Regressive and generative neural networks for scalar field theory"},"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:1810.12879."}