{"as_of":"2026-08-08T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:069e202e2786ed7f10832223251b844dc8de6a8537d368c178d73d4e2c848156","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":"2026-08-07T14:20:36.669857Z","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":"2026-05-19T09:12:14.689207Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.13777","last_updated":"2024-05-26T00:23:47Z","snapshot_observed_at":"2026-07-06T17:33:25.448648Z","submitted_at":"2024-02-21T12:54:48Z","title":"Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13777","snapshot_observed_at":"2026-08-07T14:20:36.669857Z","title":"Deep generative models for offline policy learning: Tutorial, survey, and perspectives on future directions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20350","last_updated":"2025-05-26T03:42:20Z","snapshot_observed_at":"2026-08-08T01:01:01.114971Z","submitted_at":"2025-05-26T03:42:20Z","title":"Decision Flow Policy Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:36.669857Z"},"links":{"cited_paper":"/paper/2402.13777","citing_paper":"/paper/2505.20350"},"observation_digest":"sha256:828c6cf98c8ba5b9664f645120f348b83b6497e1cdef4bd5efbaa1b037443289","observation_id":"7bbc88ad-efbd-4cf0-9dda-be2de6e039af","resolution":{"observed_at":"2026-08-07T14:20:36.669857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13777","last_updated":"2024-05-26T00:23:47Z","snapshot_observed_at":"2026-07-06T17:33:25.448648Z","submitted_at":"2024-02-21T12:54:48Z","title":"Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions","version":5},"cited_work":{"arxiv_id":"2402.13777","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.13777","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep generative models for offline policy learning: Tutorial, survey, and perspectives on future directions","venue":null,"work_id":"6e96fa86-88f2-4912-b21b-f9f4393135ea","year":2024},"citing_paper":{"arxiv_id":"2506.12622","last_updated":"2026-04-18T21:54:34Z","snapshot_observed_at":"2026-08-02T08:28:01.941809Z","submitted_at":"2025-06-14T20:36:44Z","title":"DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-19T09:09:51.531488Z"},"links":{"cited_paper":"/paper/2402.13777","citing_paper":"/paper/2506.12622"},"observation_digest":"sha256:cace636a3fa4a414c5121352db41c15bd5871415271567b010246440053a441c","observation_id":"cc276bc0-73ed-44cc-b2e5-05a00e1e9b09","resolution":{"observed_at":"2026-05-19T09:12:14.692671Z","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/2402.13777/citation-record","integrity":"/paper/2402.13777/integrity","json":"/paper/2402.13777/citation-record.json","paper":"/paper/2402.13777"},"outbound":[],"paper":{"arxiv_id":"2402.13777","last_updated":"2024-05-26T00:23:47Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:33:25.448648Z","submitted_at":"2024-02-21T12:54:48Z","title":"Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions"},"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:2402.13777."}