{"as_of":"2026-08-18T21:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0452e5fd259e2638f809a606594e9eeff063fdffb32099d3171c4e116ec09066","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":"2026-08-18T06:34:40.430872+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":"2026-08-12T18:43:02.774690Z","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":"2026-07-04T14:19:53.560639Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.02576","last_updated":"2024-06-17T16:04:27Z","snapshot_observed_at":"2026-08-17T23:23:56.346746Z","submitted_at":"2024-01-04T23:44:35Z","title":"t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02576","snapshot_observed_at":"2026-08-12T18:43:02.774690Z","title":"t-dgr: A trajectory-based deep generative replay method for continual learning in decision making","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.11364","last_updated":"2024-11-18T08:20:21Z","snapshot_observed_at":"2026-08-16T20:13:40.082991Z","submitted_at":"2024-11-18T08:20:21Z","title":"Continual Task Learning through Adaptive Policy Self-Composition","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:43:02.774690Z"},"links":{"cited_paper":"/paper/2401.02576","citing_paper":"/paper/2411.11364"},"observation_digest":"sha256:62f279268e738c2c8b1f4948665985cb33bfcf0f5a0ee1389d4a2a85bed24db2","observation_id":"31a53479-bd7f-4433-9596-05097faefc12","resolution":{"observed_at":"2026-08-12T18:43:02.774690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02576","last_updated":"2024-06-17T16:04:27Z","snapshot_observed_at":"2026-08-17T23:23:56.346746Z","submitted_at":"2024-01-04T23:44:35Z","title":"t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making","version":2},"cited_work":{"arxiv_id":"2401.02576","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.02576","snapshot_observed_at":"2026-07-04T14:19:53.560639Z","title":null,"venue":null,"work_id":"08d88cf1-6485-4855-8012-a9546bba9c89","year":2024},"citing_paper":{"arxiv_id":"2606.27374","last_updated":"2026-06-25T17:59:56Z","snapshot_observed_at":"2026-08-17T03:30:26.665668Z","submitted_at":"2026-06-25T17:59:56Z","title":"World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T04:20:26.522615Z"},"links":{"cited_paper":"/paper/2401.02576","citing_paper":"/paper/2606.27374"},"observation_digest":"sha256:2ca44eb2d0648d8d888ed1e56191994461ca9408c640ddb816d8a3c05fa0678d","observation_id":"b1de6fd7-0108-479b-b234-1a631961ab14","resolution":{"observed_at":"2026-07-04T14:19:53.563520Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.02576/citation-record","integrity":"/paper/2401.02576/integrity","json":"/paper/2401.02576/citation-record.json","paper":"/paper/2401.02576"},"outbound":[],"paper":{"arxiv_id":"2401.02576","last_updated":"2024-06-17T16:04:27Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T23:23:56.346746Z","submitted_at":"2024-01-04T23:44:35Z","title":"t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.02576."}