{"as_of":"2026-08-19T22:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9539afb87fec25304b3336067363beb267cede7bfaea824155afd15834cf4242","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:02:20.718465Z","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":"2026-05-19T10:52:15.292248Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative Replay","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18082","snapshot_observed_at":"2026-08-08T13:21:18.420119Z","title":"Prioritized generative replay","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07279","last_updated":"2025-05-16T17:18:02Z","snapshot_observed_at":"2026-08-14T17:42:32.702857Z","submitted_at":"2025-02-11T05:48:51Z","title":"Exploratory Diffusion Model for Unsupervised Reinforcement Learning","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-08T13:21:18.420119Z"},"links":{"cited_paper":"/paper/2410.18082","citing_paper":"/paper/2502.07279"},"observation_digest":"sha256:f83eb63c833147f8a318a23f5f583d33aed1244faecfdcda4326d69e08b1f662","observation_id":"bce0a378-af0f-4c70-a587-9312f03ea012","resolution":{"observed_at":"2026-08-08T13:21:18.420119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative Replay","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18082","snapshot_observed_at":"2026-08-16T11:02:20.718465Z","title":"Prioritized generative replay","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16763","last_updated":"2025-04-23T14:34:20Z","snapshot_observed_at":"2026-08-18T09:03:39.629992Z","submitted_at":"2025-04-23T14:34:20Z","title":"Noise-Tolerant Coreset-Based Class Incremental Continual Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-16T11:02:20.718465Z"},"links":{"cited_paper":"/paper/2410.18082","citing_paper":"/paper/2504.16763"},"observation_digest":"sha256:351ec28c3b9ddb07908b63876bd1174404d52e26d88fff7527bf001417f15f54","observation_id":"c9c1b940-b89b-4e4a-9d2e-dff85178b9a4","resolution":{"observed_at":"2026-08-16T11:02:20.718465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative Replay","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18082","snapshot_observed_at":"2026-08-16T04:14:13.182696Z","title":"Wang, K., Zhao, H., Luo, X., Ren, K., Zhang, W., and Li, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.01822","last_updated":"2025-05-03T14:00:25Z","snapshot_observed_at":"2026-08-18T08:58:38.785269Z","submitted_at":"2025-05-03T14:00:25Z","title":"Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T04:14:13.182696Z"},"links":{"cited_paper":"/paper/2410.18082","citing_paper":"/paper/2505.01822"},"observation_digest":"sha256:5f8269f387afab0d8a3b8d96a3370a7e369106a702302f2bf1d03dda6baab856","observation_id":"17f925d4-7135-4220-b4df-94dcc911b718","resolution":{"observed_at":"2026-08-16T04:14:13.182696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative Replay","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18082","snapshot_observed_at":"2026-08-07T14:20:41.144029Z","title":"Prioritized generative replay","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20350","last_updated":"2025-05-26T03:42:20Z","snapshot_observed_at":"2026-08-12T00:42:47.737693Z","submitted_at":"2025-05-26T03:42:20Z","title":"Decision Flow Policy Optimization","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:41.144029Z"},"links":{"cited_paper":"/paper/2410.18082","citing_paper":"/paper/2505.20350"},"observation_digest":"sha256:b1dafc1c81431ea4d5468f40c17afd30203911d51d06c6e5db7c151820d93b78","observation_id":"e161bd7c-6c5e-48ef-a647-89c905ddf6f5","resolution":{"observed_at":"2026-08-07T14:20:41.144029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative Replay","version":2},"cited_work":{"arxiv_id":"2410.18082","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.18082","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"9915b139-e091-4305-91b3-d1660f21c2cd","year":2024},"citing_paper":{"arxiv_id":"2506.05762","last_updated":"2026-05-14T17:01:38Z","snapshot_observed_at":"2026-08-15T00:07:14.719526Z","submitted_at":"2025-06-06T05:41:33Z","title":"BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning","version":5},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-19T10:48:28.980868Z"},"links":{"cited_paper":"/paper/2410.18082","citing_paper":"/paper/2506.05762"},"observation_digest":"sha256:c479ac091e33c659fbf2e0b4b30d248e05a16e5fed55fb055b676e8ad80f2ff5","observation_id":"b7f1c97b-30ed-4ad2-a29c-26781fdfb1e4","resolution":{"observed_at":"2026-05-19T10:52:15.293768Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative Replay","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18082","snapshot_observed_at":"2026-08-06T04:39:08.778688Z","title":"Prioritized generative replay","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03194","last_updated":"2025-08-05T08:03:12Z","snapshot_observed_at":"2026-08-11T12:45:17.685609Z","submitted_at":"2025-08-05T08:03:12Z","title":"Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T04:39:08.778688Z"},"links":{"cited_paper":"/paper/2410.18082","citing_paper":"/paper/2508.03194"},"observation_digest":"sha256:f8d604ebb4ef49a0a91df465a8820b872b83a4fe0942a262db7d7b978332c8b6","observation_id":"337e92b0-c427-4c5a-89f0-58d39828bdf0","resolution":{"observed_at":"2026-08-06T04:39:08.778688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2410.18082/citation-record","integrity":"/paper/2410.18082/integrity","json":"/paper/2410.18082/citation-record.json","paper":"/paper/2410.18082"},"outbound":[],"paper":{"arxiv_id":"2410.18082","last_updated":"2025-05-08T23:56:41Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T09:03:48.229516Z","submitted_at":"2024-10-23T17:59:52Z","title":"Prioritized Generative 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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.18082."}