{"as_of":"2026-08-09T05:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c28dd2f8704fe22a73594da860c0e319483ad3b36f9e964706274e2a0081ee7a","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:03:15.071223Z","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":"2026-08-06T15:00:57.730134Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.00887","last_updated":"2019-11-22T07:12:00Z","snapshot_observed_at":"2026-07-06T08:34:21.494347Z","submitted_at":"2019-11-03T13:44:42Z","title":"Online Robustness Training for Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00887","snapshot_observed_at":"2026-08-07T05:03:15.071223Z","title":"Online ro- bustness training for deep reinforcement learning,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2506.08961","last_updated":"2025-06-10T16:32:31Z","snapshot_observed_at":"2026-08-07T04:55:47.839318Z","submitted_at":"2025-06-10T16:32:31Z","title":"Towards Robust Deep Reinforcement Learning against Environmental State Perturbation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:03:15.071223Z"},"links":{"cited_paper":"/paper/1911.00887","citing_paper":"/paper/2506.08961"},"observation_digest":"sha256:0242ae80d98956fdf8c0cd9532f67ffe6ea7fbff93cb3d8741cf0f9eb5ec86e4","observation_id":"93235302-0c37-49af-8fb0-29ccd104eaa9","resolution":{"observed_at":"2026-08-07T05:03:15.071223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00887","last_updated":"2019-11-22T07:12:00Z","snapshot_observed_at":"2026-07-06T08:34:21.494347Z","submitted_at":"2019-11-03T13:44:42Z","title":"Online Robustness Training for Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00887","snapshot_observed_at":"2026-08-06T20:25:07.041555Z","title":"Online robustness training for deep reinforcement learning","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.03372","last_updated":"2025-07-04T08:11:15Z","snapshot_observed_at":"2026-08-06T20:33:21.286270Z","submitted_at":"2025-07-04T08:11:15Z","title":"Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:25:07.041555Z"},"links":{"cited_paper":"/paper/1911.00887","citing_paper":"/paper/2507.03372"},"observation_digest":"sha256:cc8f5352f77f0c1a9113011a1a927ade74691fd9486376f24a51b7bf7ffd0dfc","observation_id":"7d63bf7e-49bc-4d6c-b52e-e2b52d525f58","resolution":{"observed_at":"2026-08-06T20:25:07.041555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00887","last_updated":"2019-11-22T07:12:00Z","snapshot_observed_at":"2026-07-06T08:34:21.494347Z","submitted_at":"2019-11-03T13:44:42Z","title":"Online Robustness Training for Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1911.00887","doi":null,"metadata_source":"pith","pith_arxiv_id":"1911.00887","snapshot_observed_at":"2026-08-06T15:00:57.730134Z","title":"Online Robustness Training for Deep Reinforcement Learning","venue":"cs.LG","work_id":"8a915bb2-8866-4356-9299-690503408e69","year":2019},"citing_paper":{"arxiv_id":"2507.17070","last_updated":"2025-07-22T23:15:11Z","snapshot_observed_at":"2026-08-07T22:20:04.873484Z","submitted_at":"2025-07-22T23:15:11Z","title":"Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:00:57.663825Z"},"links":{"cited_paper":"/paper/1911.00887","citing_paper":"/paper/2507.17070"},"observation_digest":"sha256:b4832ffadee94f10a029bbaa996656440ae16d040d9112b78cf9de9f5284f513","observation_id":"9ccd6462-8518-4031-bcf8-dcc6cad74966","resolution":{"observed_at":"2026-08-06T15:00:57.737034Z","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/1911.00887/citation-record","integrity":"/paper/1911.00887/integrity","json":"/paper/1911.00887/citation-record.json","paper":"/paper/1911.00887"},"outbound":[],"paper":{"arxiv_id":"1911.00887","last_updated":"2019-11-22T07:12:00Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:34:21.494347Z","submitted_at":"2019-11-03T13:44:42Z","title":"Online Robustness Training for Deep Reinforcement Learning"},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1911.00887."}