{"as_of":"2026-08-22T12:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:79fa79f1759737f1cb8ea954847c85ec14bd59ae452f88e88b75aab72d0c68a5","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":"2026-08-22T06:32:14.747728+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":"2026-08-09T12:52:42.538706Z","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":"2026-08-09T12:52:42.708367Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.07694","last_updated":"2025-08-06T02:38:08Z","snapshot_observed_at":"2026-08-21T17:07:42.320847Z","submitted_at":"2023-07-15T03:12:11Z","title":"Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation","version":3},"cited_work":{"arxiv_id":"2307.07694","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.07694","snapshot_observed_at":"2026-08-09T12:52:42.708367Z","title":"Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation","venue":"cs.CE","work_id":"c50cc51d-7bd2-4916-9a2c-adce34acaf96","year":2023},"citing_paper":{"arxiv_id":"2502.02619","last_updated":"2025-02-04T11:45:59Z","snapshot_observed_at":"2026-08-21T14:50:20.348541Z","submitted_at":"2025-02-04T11:45:59Z","title":"Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T12:52:42.538706Z"},"links":{"cited_paper":"/paper/2307.07694","citing_paper":"/paper/2502.02619"},"observation_digest":"sha256:5a0cfb1aa859eaf57b17ac50757d4b1a9a11093f4317e994df974d06a36f8940","observation_id":"f800ead4-2d1a-48a0-930c-1318f54c2fea","resolution":{"observed_at":"2026-08-09T12:52:42.712066Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2307.07694/citation-record","integrity":"/paper/2307.07694/integrity","json":"/paper/2307.07694/citation-record.json","paper":"/paper/2307.07694"},"outbound":[],"paper":{"arxiv_id":"2307.07694","last_updated":"2025-08-06T02:38:08Z","latest_version":3,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-21T17:07:42.320847Z","submitted_at":"2023-07-15T03:12:11Z","title":"Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2307.07694."}