{"as_of":"2026-08-13T12:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:69915422a4a0c17bb12e881b8d100bcca97dc84df0858e0c21a4d83d38421215","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":"2026-08-13T06:32:02.005865+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":"2026-08-10T20:37:04.123256Z","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":"2026-08-07T04:59:18.828658Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.07530","last_updated":"2020-04-16T08:47:40Z","snapshot_observed_at":"2026-07-06T09:12:47.190763Z","submitted_at":"2020-04-16T08:47:40Z","title":"Continual Reinforcement Learning with Multi-Timescale Replay","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.07530","snapshot_observed_at":"2026-08-10T20:37:04.123256Z","title":"Continual reinforcement learning with multi-timescale replay,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2501.08045","last_updated":"2025-02-22T05:21:16Z","snapshot_observed_at":"2026-08-13T04:40:44.376157Z","submitted_at":"2025-01-14T11:53:07Z","title":"Continual Reinforcement Learning for Digital Twin Synchronization Optimization","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T20:37:04.123256Z"},"links":{"cited_paper":"/paper/2004.07530","citing_paper":"/paper/2501.08045"},"observation_digest":"sha256:789c5af4d0f134ab4cc32fc115ec18b81010ac3d581fda8eb64622ce59fd021e","observation_id":"e37cc7a5-09f5-448c-b436-1ae03ee72c1c","resolution":{"observed_at":"2026-08-10T20:37:04.123256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.07530","last_updated":"2020-04-16T08:47:40Z","snapshot_observed_at":"2026-07-06T09:12:47.190763Z","submitted_at":"2020-04-16T08:47:40Z","title":"Continual Reinforcement Learning with Multi-Timescale Replay","version":1},"cited_work":{"arxiv_id":"2004.07530","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.07530","snapshot_observed_at":"2026-08-07T04:59:18.828658Z","title":"Continual Reinforcement Learning with Multi-Timescale Replay","venue":"cs.LG","work_id":"22bff7a0-09a3-4604-a1ef-802a47868b25","year":2020},"citing_paper":{"arxiv_id":"2506.09270","last_updated":"2025-06-10T22:07:13Z","snapshot_observed_at":"2026-08-09T14:59:59.898677Z","submitted_at":"2025-06-10T22:07:13Z","title":"Uncertainty Prioritized Experience Replay","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T04:59:08.646940Z"},"links":{"cited_paper":"/paper/2004.07530","citing_paper":"/paper/2506.09270"},"observation_digest":"sha256:7f00af7ceb7de69f4d5d383569c444cdda6922f29657bc3cd2eae81562952748","observation_id":"756025c3-709f-4a6f-bcd1-122a99c94773","resolution":{"observed_at":"2026-08-07T04:59:18.904049Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2004.07530/citation-record","integrity":"/paper/2004.07530/integrity","json":"/paper/2004.07530/citation-record.json","paper":"/paper/2004.07530"},"outbound":[],"paper":{"arxiv_id":"2004.07530","last_updated":"2020-04-16T08:47:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:12:47.190763Z","submitted_at":"2020-04-16T08:47:40Z","title":"Continual Reinforcement Learning with Multi-Timescale Replay"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2004.07530."}