{"as_of":"2026-08-11T04:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e77bbecfdfa0448fe23821e4355336b2e468b0bfadba95cde347c3c1df9b73a1","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":"2026-08-10T06:31:04.303077+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":"2026-08-06T18:36:40.897742Z","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":"2026-05-21T07:09:46.487078Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2101.07107","last_updated":"2023-08-19T08:10:38Z","snapshot_observed_at":"2026-08-10T23:12:30.964102Z","submitted_at":"2021-01-18T15:09:28Z","title":"Deep Reinforcement Learning for Active High Frequency Trading","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.07107","snapshot_observed_at":"2026-08-06T18:36:40.897742Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.07848","last_updated":"2025-07-29T08:20:12Z","snapshot_observed_at":"2026-08-10T01:42:24.583381Z","submitted_at":"2025-07-10T15:27:44Z","title":"\"So, Tell Me About Your Policy...\": Distillation of interpretable policies from Deep Reinforcement Learning agents","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T18:36:40.897742Z"},"links":{"cited_paper":"/paper/2101.07107","citing_paper":"/paper/2507.07848"},"observation_digest":"sha256:4634fef8af9065088f569180ff20b166e1145279ae9e20a4ef1d9a7ae0fbf848","observation_id":"e18f7011-50c9-4c5c-a035-9cb088ecb4d3","resolution":{"observed_at":"2026-08-06T18:36:40.897742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.07107","last_updated":"2023-08-19T08:10:38Z","snapshot_observed_at":"2026-08-10T23:12:30.964102Z","submitted_at":"2021-01-18T15:09:28Z","title":"Deep Reinforcement Learning for Active High Frequency Trading","version":3},"cited_work":{"arxiv_id":"2101.07107","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2101.07107","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2101.07107 , year=","venue":null,"work_id":"ff142cc8-b1a3-4a6d-b1de-ed0cc9ab4121","year":null},"citing_paper":{"arxiv_id":"2605.20348","last_updated":"2026-05-19T18:03:48Z","snapshot_observed_at":"2026-08-02T15:33:17.310428Z","submitted_at":"2026-05-19T18:03:48Z","title":"Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-21T07:05:07.816803Z"},"links":{"cited_paper":"/paper/2101.07107","citing_paper":"/paper/2605.20348"},"observation_digest":"sha256:943b9e34aae8ba1abef1ecb98e6b2f8dc4ba199a415ec1b95205682d6ca9a20f","observation_id":"1ccfceb6-7877-4685-9c48-0f8eb6682592","resolution":{"observed_at":"2026-05-21T07:09:46.488849Z","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/2101.07107/citation-record","integrity":"/paper/2101.07107/integrity","json":"/paper/2101.07107/citation-record.json","paper":"/paper/2101.07107"},"outbound":[],"paper":{"arxiv_id":"2101.07107","last_updated":"2023-08-19T08:10:38Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T23:12:30.964102Z","submitted_at":"2021-01-18T15:09:28Z","title":"Deep Reinforcement Learning for Active High Frequency Trading"},"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 2 inbound Pith citation observations for arXiv:2101.07107."}