{"as_of":"2026-08-14T17:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54c5b9f9bb6dc554afb53781e59bc4fd39cd33239d905a936b6e7f91abf9055f","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":"2026-08-14T06:32:32.682623+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":"2026-05-12T03:00:03.443063Z","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":"2026-05-12T03:01:17.802354Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.14566","last_updated":"2024-01-15T06:09:01Z","snapshot_observed_at":"2026-08-14T12:21:40.001034Z","submitted_at":"2022-12-30T06:47:03Z","title":"Pontryagin Optimal Control via Neural Networks","version":3},"cited_work":{"arxiv_id":"2212.14566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.14566","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2212.14566 , year=","venue":null,"work_id":"bd8ec7b2-4e62-49b9-ae76-d9c994a20755","year":null},"citing_paper":{"arxiv_id":"2605.05373","last_updated":"2026-05-11T11:51:17Z","snapshot_observed_at":"2026-07-30T04:00:33.730388Z","submitted_at":"2026-05-06T18:53:33Z","title":"Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-08T17:53:08.408400Z"},"links":{"cited_paper":"/paper/2212.14566","citing_paper":"/paper/2605.05373"},"observation_digest":"sha256:397b5306da764c9f259b75334eb44b15b0d83300d939e3fac15d99d4dd464d47","observation_id":"ddf875df-6caf-47ad-86bb-47bc8a4497d4","resolution":{"observed_at":"2026-05-11T17:06:05.601710Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14566","last_updated":"2024-01-15T06:09:01Z","snapshot_observed_at":"2026-08-14T12:21:40.001034Z","submitted_at":"2022-12-30T06:47:03Z","title":"Pontryagin Optimal Control via Neural Networks","version":3},"cited_work":{"arxiv_id":"2212.14566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.14566","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2212.14566 , year=","venue":null,"work_id":"bd8ec7b2-4e62-49b9-ae76-d9c994a20755","year":null},"citing_paper":{"arxiv_id":"2605.05373","last_updated":"2026-05-11T11:51:17Z","snapshot_observed_at":"2026-07-30T04:00:33.730388Z","submitted_at":"2026-05-06T18:53:33Z","title":"Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-12T03:00:03.443063Z"},"links":{"cited_paper":"/paper/2212.14566","citing_paper":"/paper/2605.05373"},"observation_digest":"sha256:880c2d9e51bf1e6c4ee10e925a70af481021e59d780dec5605198ced768f4534","observation_id":"1bf8f778-f062-4278-bf80-bc84e5619deb","resolution":{"observed_at":"2026-05-12T03:01:17.803885Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2212.14566/citation-record","integrity":"/paper/2212.14566/integrity","json":"/paper/2212.14566/citation-record.json","paper":"/paper/2212.14566"},"outbound":[],"paper":{"arxiv_id":"2212.14566","last_updated":"2024-01-15T06:09:01Z","latest_version":3,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-14T12:21:40.001034Z","submitted_at":"2022-12-30T06:47:03Z","title":"Pontryagin Optimal Control via 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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.14566."}