{"as_of":"2026-08-19T02:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b1352fc4876ea2b8e7f315146c37e569841b9020d3e7d68f97c69f160748db0f","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T14:48:31.977214Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1809.05989","last_updated":"2018-11-13T19:14:50Z","snapshot_observed_at":"2026-08-14T18:27:43.036036Z","submitted_at":"2018-09-17T01:26:57Z","title":"FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.05989","snapshot_observed_at":"2026-08-02T14:48:31.977214Z","title":"Ferminets: Learning generative machines to generate efficient neural networks via generative synthesis","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.05540","last_updated":"2026-07-27T15:13:41Z","snapshot_observed_at":"2026-08-17T21:06:27.664747Z","submitted_at":"2026-05-07T00:41:47Z","title":"Autoregressive One-Step Generative Modeling for Dynamical System Forecasting","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T14:48:31.977214Z"},"links":{"cited_paper":"/paper/1809.05989","citing_paper":"/paper/2605.05540"},"observation_digest":"sha256:34684828378f5da46789682d30aa51f4b55360a7f6a32f645de79822db4b23ed","observation_id":"e73d1b38-f48e-425b-87e1-73bc45e0646e","resolution":{"observed_at":"2026-08-02T14:48:31.977214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1809.05989/citation-record","integrity":"/paper/1809.05989/integrity","json":"/paper/1809.05989/citation-record.json","paper":"/paper/1809.05989"},"outbound":[],"paper":{"arxiv_id":"1809.05989","last_updated":"2018-11-13T19:14:50Z","latest_version":2,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-14T18:27:43.036036Z","submitted_at":"2018-09-17T01:26:57Z","title":"FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1809.05989."}