{"as_of":"2026-08-11T01:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19076abae79113f1a745c8d077525fc98a8ca89fb82a51cc815c7da988608a13","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":"2026-08-10T06:31:04.303077+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":"2026-06-25T21:20:42.768437Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T19:30:07.022190Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.14973","last_updated":"2025-02-19T19:07:52Z","snapshot_observed_at":"2026-08-10T19:25:21.011481Z","submitted_at":"2024-05-23T18:19:47Z","title":"Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality","version":2},"cited_work":{"arxiv_id":"2405.14973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.14973","snapshot_observed_at":"2026-07-04T19:30:07.022190Z","title":"arXiv preprint arXiv:2405.14973 , year=","venue":null,"work_id":"c8f5a1f5-9282-498e-8d63-fd400605053b","year":null},"citing_paper":{"arxiv_id":"2606.25362","last_updated":"2026-06-24T03:45:52Z","snapshot_observed_at":"2026-07-06T23:59:47.542695Z","submitted_at":"2026-06-24T03:45:52Z","title":"Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-06-25T21:20:42.768437Z"},"links":{"cited_paper":"/paper/2405.14973","citing_paper":"/paper/2606.25362"},"observation_digest":"sha256:01721d59e1e3c3483d3a2626fb1d75e4d4e9f149c419f0525229957f2aecebc1","observation_id":"075fde9d-c243-4fed-8228-75cd4f406142","resolution":{"observed_at":"2026-07-04T19:30:07.023969Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.14973/citation-record","integrity":"/paper/2405.14973/integrity","json":"/paper/2405.14973/citation-record.json","paper":"/paper/2405.14973"},"outbound":[],"paper":{"arxiv_id":"2405.14973","last_updated":"2025-02-19T19:07:52Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T19:25:21.011481Z","submitted_at":"2024-05-23T18:19:47Z","title":"Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2405.14973."}