{"as_of":"2026-08-08T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:93bce3b37d28e5df9664a2419743d2dbda336974ed864acd6e5942a152313813","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":"2026-08-08T06:32:00.761636+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":"2026-08-05T21:34:30.394721Z","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":"2026-07-04T07:09:37.901601Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.04974","last_updated":"2023-07-20T13:11:13Z","snapshot_observed_at":"2026-07-06T14:16:17.263268Z","submitted_at":"2022-11-09T15:39:32Z","title":"Leveraging Offline Data in Online Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.04974","snapshot_observed_at":"2026-08-05T21:34:30.394721Z","title":"Leveraging offline data in online reinforcement learning.arXiv preprint arXiv:2211.04974,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.08413","last_updated":"2025-08-11T19:07:30Z","snapshot_observed_at":"2026-08-07T12:16:45.804784Z","submitted_at":"2025-08-11T19:07:30Z","title":"Decentralized Relaxed Smooth Optimization with Gradient Descent Methods","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T21:34:30.394721Z"},"links":{"cited_paper":"/paper/2211.04974","citing_paper":"/paper/2508.08413"},"observation_digest":"sha256:a88db9d57bc571a8c4f61d55ea299be547171dc96a0c8bed3e0e098f8bd45205","observation_id":"646788d6-5594-40d9-a671-2ba2e2fb386a","resolution":{"observed_at":"2026-08-05T21:34:30.394721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.04974","last_updated":"2023-07-20T13:11:13Z","snapshot_observed_at":"2026-07-06T14:16:17.263268Z","submitted_at":"2022-11-09T15:39:32Z","title":"Leveraging Offline Data in Online Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2211.04974","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.04974","snapshot_observed_at":"2026-07-04T07:09:37.901601Z","title":"arXiv preprint arXiv:2211.04974 , year=","venue":null,"work_id":"62807a2c-4b98-46e0-af9b-bd02a075d5ee","year":null},"citing_paper":{"arxiv_id":"2606.21483","last_updated":"2026-06-19T14:34:17Z","snapshot_observed_at":"2026-08-07T12:30:36.186965Z","submitted_at":"2026-06-19T14:34:17Z","title":"A note on the convergence guarantees of RLT-based algorithms for polynomial optimization","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-06-26T13:44:16.025930Z"},"links":{"cited_paper":"/paper/2211.04974","citing_paper":"/paper/2606.21483"},"observation_digest":"sha256:cedafcedb87d414a7725de8035d2be1786617bf2f40b1f436baa81fa34460b7a","observation_id":"78dde8ef-ae3b-4c4a-8a30-613ce50c987c","resolution":{"observed_at":"2026-07-04T07:09:37.903387Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2211.04974/citation-record","integrity":"/paper/2211.04974/integrity","json":"/paper/2211.04974/citation-record.json","paper":"/paper/2211.04974"},"outbound":[],"paper":{"arxiv_id":"2211.04974","last_updated":"2023-07-20T13:11:13Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:16:17.263268Z","submitted_at":"2022-11-09T15:39:32Z","title":"Leveraging Offline Data in Online Reinforcement Learning"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2211.04974."}