{"as_of":"2026-08-08T05:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2d589ef450b501e66699d282f7a6043201ba519e271b1a45e8e4e93e8a7fc7e6","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T10:50:40.841967Z","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":"2026-07-04T08:49:42.788554Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2008.01825","last_updated":"2020-09-22T22:41:54Z","snapshot_observed_at":"2026-07-06T09:44:36.129510Z","submitted_at":"2020-08-04T20:57:32Z","title":"Robust Reinforcement Learning using Adversarial Populations","version":2},"cited_work":{"arxiv_id":"2008.01825","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.01825","snapshot_observed_at":"2026-07-04T08:49:42.788554Z","title":"Robust reinforcement learning using adversar- ial populations.arXiv preprint arXiv:2008.01825","venue":null,"work_id":"6031d7cb-018c-479b-b20f-332e3daf798f","year":2008},"citing_paper":{"arxiv_id":"2502.02844","last_updated":"2025-06-18T06:49:09Z","snapshot_observed_at":"2026-07-06T20:31:17.809419Z","submitted_at":"2025-02-05T02:59:23Z","title":"Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-23T03:24:33.788346Z"},"links":{"cited_paper":"/paper/2008.01825","citing_paper":"/paper/2502.02844"},"observation_digest":"sha256:b8f04e43458b4a893ee17f6c77e0f421aeac3aa38c400061dab2bd767230a606","observation_id":"cb336866-af48-4b62-b504-530e9a45882e","resolution":{"observed_at":"2026-05-23T03:25:20.422549Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.01825","last_updated":"2020-09-22T22:41:54Z","snapshot_observed_at":"2026-07-06T09:44:36.129510Z","submitted_at":"2020-08-04T20:57:32Z","title":"Robust Reinforcement Learning using Adversarial Populations","version":2},"cited_work":{"arxiv_id":"2008.01825","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.01825","snapshot_observed_at":"2026-07-04T08:49:42.788554Z","title":"Robust reinforcement learning using adversar- ial populations.arXiv preprint arXiv:2008.01825","venue":null,"work_id":"6031d7cb-018c-479b-b20f-332e3daf798f","year":2008},"citing_paper":{"arxiv_id":"2602.08813","last_updated":"2026-05-12T17:22:35Z","snapshot_observed_at":"2026-08-06T12:22:42.069570Z","submitted_at":"2026-02-09T15:50:05Z","title":"Robust Policy Optimization to Prevent Catastrophic Forgetting","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-16T05:33:42.965249Z"},"links":{"cited_paper":"/paper/2008.01825","citing_paper":"/paper/2602.08813"},"observation_digest":"sha256:22a74f1334984e7701e9f3a6e121776fc365247a74c89dfcc7d694adcaaae2c6","observation_id":"5cb617dd-44fd-407f-a252-7838b2959ade","resolution":{"observed_at":"2026-05-16T05:37:24.352963Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.01825","last_updated":"2020-09-22T22:41:54Z","snapshot_observed_at":"2026-07-06T09:44:36.129510Z","submitted_at":"2020-08-04T20:57:32Z","title":"Robust Reinforcement Learning using Adversarial Populations","version":2},"cited_work":{"arxiv_id":"2008.01825","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.01825","snapshot_observed_at":"2026-07-04T08:49:42.788554Z","title":"Robust reinforcement learning using adversar- ial populations.arXiv preprint arXiv:2008.01825","venue":null,"work_id":"6031d7cb-018c-479b-b20f-332e3daf798f","year":2008},"citing_paper":{"arxiv_id":"2604.10974","last_updated":"2026-04-15T07:15:04Z","snapshot_observed_at":"2026-07-31T13:13:30.847119Z","submitted_at":"2026-04-13T04:23:54Z","title":"Robust Adversarial Policy Optimization Under Dynamics Uncertainty","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T16:00:33.267264Z"},"links":{"cited_paper":"/paper/2008.01825","citing_paper":"/paper/2604.10974"},"observation_digest":"sha256:6e56387c31bbd1931351b92754888ab0bcdc0df6e69657ff4cc57eac834a7e97","observation_id":"bbe5da3d-ddfa-4453-8b81-686c77b6fe1a","resolution":{"observed_at":"2026-05-11T09:26:03.620880Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.01825","last_updated":"2020-09-22T22:41:54Z","snapshot_observed_at":"2026-07-06T09:44:36.129510Z","submitted_at":"2020-08-04T20:57:32Z","title":"Robust Reinforcement Learning using Adversarial Populations","version":2},"cited_work":{"arxiv_id":"2008.01825","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.01825","snapshot_observed_at":"2026-07-04T08:49:42.788554Z","title":"Robust reinforcement learning using adversar- ial populations.arXiv preprint arXiv:2008.01825","venue":null,"work_id":"6031d7cb-018c-479b-b20f-332e3daf798f","year":2008},"citing_paper":{"arxiv_id":"2604.14243","last_updated":"2026-04-17T15:08:39Z","snapshot_observed_at":"2026-07-06T23:02:05.116649Z","submitted_at":"2026-04-15T04:53:29Z","title":"Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T13:36:59.355147Z"},"links":{"cited_paper":"/paper/2008.01825","citing_paper":"/paper/2604.14243"},"observation_digest":"sha256:651d7e8bd39986e205401c82c793f54a0cef7415963f581db0e0160e7235a053","observation_id":"ea523736-29b2-4300-bd4e-97bbcbf9ba27","resolution":{"observed_at":"2026-05-10T13:40:27.166339Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.01825","last_updated":"2020-09-22T22:41:54Z","snapshot_observed_at":"2026-07-06T09:44:36.129510Z","submitted_at":"2020-08-04T20:57:32Z","title":"Robust Reinforcement Learning using Adversarial Populations","version":2},"cited_work":{"arxiv_id":"2008.01825","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2008.01825","snapshot_observed_at":"2026-07-04T08:49:42.788554Z","title":"Robust reinforcement learning using adversar- ial populations.arXiv preprint arXiv:2008.01825","venue":null,"work_id":"6031d7cb-018c-479b-b20f-332e3daf798f","year":2008},"citing_paper":{"arxiv_id":"2606.22579","last_updated":"2026-06-21T16:29:25Z","snapshot_observed_at":"2026-08-02T07:11:59.062207Z","submitted_at":"2026-06-21T16:29:25Z","title":"Stationary Robust Mean-Field Games under Model Mismatches","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-06-26T10:50:40.841967Z"},"links":{"cited_paper":"/paper/2008.01825","citing_paper":"/paper/2606.22579"},"observation_digest":"sha256:7eba7750ce112734d5e6961b2975403bf4ca63174301f647cf1baf3726ff1cb6","observation_id":"d58fca51-a31e-4fd6-b32d-276979038c38","resolution":{"observed_at":"2026-07-04T08:49:42.790059Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2008.01825/citation-record","integrity":"/paper/2008.01825/integrity","json":"/paper/2008.01825/citation-record.json","paper":"/paper/2008.01825"},"outbound":[],"paper":{"arxiv_id":"2008.01825","last_updated":"2020-09-22T22:41:54Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:44:36.129510Z","submitted_at":"2020-08-04T20:57:32Z","title":"Robust Reinforcement Learning using Adversarial Populations"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2008.01825."}