{"as_of":"2026-08-08T20:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:31d08843b41f9fbc9b9f4627640a6c601266f9ef04bf842975110f6d792b4a46","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":"2026-08-08T06:32:00.761636+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":"2026-08-06T20:31:07.885065Z","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":"2026-08-06T20:31:07.935311Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.09030","last_updated":"2024-06-13T12:03:40Z","snapshot_observed_at":"2026-07-06T18:30:15.196556Z","submitted_at":"2024-06-13T12:03:40Z","title":"CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms","version":1},"cited_work":{"arxiv_id":"2406.09030","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.09030","snapshot_observed_at":"2026-08-06T20:31:07.935311Z","title":"CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms","venue":"cs.LG","work_id":"24be7277-91ce-4f71-b561-0287f0932a0f","year":2024},"citing_paper":{"arxiv_id":"2507.02712","last_updated":"2025-07-03T15:26:48Z","snapshot_observed_at":"2026-08-08T15:34:11.449065Z","submitted_at":"2025-07-03T15:26:48Z","title":"A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-06T20:31:07.885065Z"},"links":{"cited_paper":"/paper/2406.09030","citing_paper":"/paper/2507.02712"},"observation_digest":"sha256:8c0994fc00cca1b89ce4b3010113107f90906fe5ff024ca7a9ed94c9f01ea344","observation_id":"d999a541-9b98-4a60-b247-3d673b20e49e","resolution":{"observed_at":"2026-08-06T20:31:07.940764Z","resolver_source":"local_arxiv","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/2406.09030/citation-record","integrity":"/paper/2406.09030/integrity","json":"/paper/2406.09030/citation-record.json","paper":"/paper/2406.09030"},"outbound":[],"paper":{"arxiv_id":"2406.09030","last_updated":"2024-06-13T12:03:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:30:15.196556Z","submitted_at":"2024-06-13T12:03:40Z","title":"CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms"},"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 1 inbound Pith citation observation for arXiv:2406.09030."}