{"as_of":"2026-08-18T09:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:48e15e02b9417fb0c1a790c202cd55336967f9ceccc90fb85a79b56e9f5ec77f","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T04:32:28.279296Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.08743/citation-record","integrity":"/paper/2608.08743/integrity","json":"/paper/2608.08743/citation-record.json","paper":"/paper/2608.08743"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.000230Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.000230Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:f35b1766f8e6a91ad195497f72eef9061225ceb6ca437965229e548449b6a126","observation_id":"6fc0fdac-d787-4a10-9182-b8e3d5cb80bb","resolution":{"observed_at":"2026-08-14T04:32:28.000230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.406760Z","title":null,"venue":null,"work_id":"2b4f0fdb-96b5-4555-abf8-d6f60aabb2f8","year":2024},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.012712Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:fe5d56c6b0056a5af084ba7e74c6d01eb8662eace6f03900d22fc13ffc974f98","observation_id":"a5ac820d-c3d2-4669-9be8-6dae87c70217","resolution":{"observed_at":"2026-08-14T04:32:29.412296Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.386371Z","title":"and Jiang, N","venue":null,"work_id":"bb4f03f5-7ad8-4425-874c-7bada1ed7274","year":2019},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.021177Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:02f2dd89cd27f512fd813b6b85622ebc613e7d9f539e735e531d3b6ffbb14129","observation_id":"08738ae3-efc4-45a7-bf4d-f94502a3a6f5","resolution":{"observed_at":"2026-08-14T04:32:29.392799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.364956Z","title":null,"venue":null,"work_id":"ac89e4f8-58ef-4f46-aabc-10a5801dcb46","year":2025},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.030145Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:6bd628a7f1a12375d3d7469581beeecd49ce72d5d5492e700bc5851964602634","observation_id":"b58e3e87-c966-48bb-9251-59b5d2181b4a","resolution":{"observed_at":"2026-08-14T04:32:29.371497Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.337091Z","title":null,"venue":null,"work_id":"084e6f98-4ee9-4ece-9262-8e210377ad13","year":2010},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.036647Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:fb03d8f9d8d5e0efa4da05f594d8e2496979892c1e0b14ebf6ec10d866f8ade3","observation_id":"14b53a59-bde5-4f45-9e2d-ef48271b5a2f","resolution":{"observed_at":"2026-08-14T04:32:29.343746Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.310712Z","title":null,"venue":null,"work_id":"5206b1ec-b610-43ef-a9d8-d00640d6c1cd","year":2013},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.042491Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:0d0d372567004d8d6e71ee5bad91c451f993aede5cdeea2b34ba018c56d128ab","observation_id":"db60f650-21fb-46d4-ba92-98c03b3baabc","resolution":{"observed_at":"2026-08-14T04:32:29.318399Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.293291Z","title":"and Hansen, C","venue":null,"work_id":"b621825f-829e-4aa1-b3e3-ad8f6c0c41d0","year":2005},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.050289Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:421b0c65ae7a4e9537a28547ce4ff48d74f3151e43ac9c863689d572a2e538d8","observation_id":"ff6fa425-5199-402c-a001-035b2d87df7c","resolution":{"observed_at":"2026-08-14T04:32:29.299083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.274457Z","title":null,"venue":null,"work_id":"4e128e63-119f-4326-a2b9-10b29722e721","year":2020},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.058448Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:4039cfbc335caa1adec44669e6d73eec856650041d59ea8876a8a6f2537a6a22","observation_id":"8d34be8b-318d-4035-b788-e408aa993c5d","resolution":{"observed_at":"2026-08-14T04:32:29.279439Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.257781Z","title":null,"venue":null,"work_id":"8ae2c248-1bff-4e47-b5bc-eddaaee918a1","year":2020},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.063979Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:584aacdee169432fe04e8e1ec32df04474b0e61c9e1d491e6eeaba0c9f2a6717","observation_id":"0cf47483-24a2-4958-8436-cffa16a59672","resolution":{"observed_at":"2026-08-14T04:32:29.263006Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10774","last_updated":"2020-02-26T11:28:34Z","snapshot_observed_at":"2026-07-06T08:59:55.421273Z","submitted_at":"2020-02-25T10:13:55Z","title":"Counterfactual fairness: removing direct effects through regularization","version":2},"cited_work":{"arxiv_id":"2002.10774","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.10774","snapshot_observed_at":"2026-08-14T04:32:28.597553Z","title":"Counterfactual fairness: removing direct effects through regularization","venue":"cs.AI","work_id":"a640907b-c52e-4d23-8e22-b4069268bd3d","year":2020},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.070219Z"},"links":{"cited_paper":"/paper/2002.10774","citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:4a3ea1a5056aa8c543b3b48b689b8fafb748a5aed71aed2417aa5fd0cff86bf1","observation_id":"85580858-90f7-443e-8f76-d73bde7c6cb9","resolution":{"observed_at":"2026-08-14T04:32:28.604866Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1502.02259","last_updated":"2015-02-08T14:58:50Z","snapshot_observed_at":"2026-08-14T23:00:56.212471Z","submitted_at":"2015-02-08T14:58:50Z","title":"Contextual Markov Decision Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.02259","snapshot_observed_at":"2026-08-14T04:32:28.076578Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.076578Z"},"links":{"cited_paper":"/paper/1502.02259","citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:f07c41c3398c73c366f89a7d03d418bcbd6e19a0e96fd4cdf8a10a97d0060854","observation_id":"025f486c-9489-4f35-8e56-6aea85894ebf","resolution":{"observed_at":"2026-08-14T04:32:28.076578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.236253Z","title":"A., Ashburn-Nardo, L., Stewart, J","venue":null,"work_id":"767ace7b-2efe-44c1-919c-2ba6c51a4e61","year":2016},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.082955Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:9d70c8c42b50577488b9786d55642eb8b616856c310301d45f6ebd8403468551","observation_id":"bf2a5fc2-4861-46f2-91f1-f95286416b47","resolution":{"observed_at":"2026-08-14T04:32:29.242765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.00479","last_updated":"2023-07-12T09:33:34Z","snapshot_observed_at":"2026-08-16T18:48:45.306796Z","submitted_at":"2021-01-31T16:17:56Z","title":"Fast Rates for the Regret of Offline Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.00479","snapshot_observed_at":"2026-08-14T04:32:28.089394Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.089394Z"},"links":{"cited_paper":"/paper/2102.00479","citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:72e4a154743482bb9a3abd40d673340f529efa79d779cb332dc656f64c3ce6e2","observation_id":"27777044-4ec5-4abb-9f4a-a508d243a5d4","resolution":{"observed_at":"2026-08-14T04:32:28.089394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.215680Z","title":null,"venue":null,"work_id":"34605ce6-1162-4ae3-b3b7-cb0edb6a14a3","year":2022},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.097153Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:f7a8e470e27ff974f792d212bcb244e062a6c0df33683254abcfe5192210937d","observation_id":"da517c1b-98b8-4487-b4fc-0af966bfac85","resolution":{"observed_at":"2026-08-14T04:32:29.223007Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.195992Z","title":"and Langford, J","venue":null,"work_id":"af78cb29-6064-4930-af1b-0d87ad230c14","year":2002},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.102982Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:1473faed204f638b7c4d4f7891542d58d18f321498193fada31fd1885cb6f88f","observation_id":"5991f9a7-9177-4456-b5e7-8b88475acb78","resolution":{"observed_at":"2026-08-14T04:32:29.201784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T04:32:28.109209Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.109209Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:8d098934d1d95e68f49b45041c0de536a5f03eb1d82d677f0919c94a35aca4e0","observation_id":"cb51405a-a3e4-4e3b-a4f7-facb0db92a0c","resolution":{"observed_at":"2026-08-14T04:32:28.109209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.177023Z","title":"(2005).Quantile Regression","venue":null,"work_id":"dc929601-c07e-4d62-b850-5849b16ea582","year":2005},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.116021Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:35b2452696a202ea1388f96922c5889050ee53a8592af8bc791ffef89c50d021","observation_id":"1b8896e4-e391-45f2-a0cd-f39b76e294f1","resolution":{"observed_at":"2026-08-14T04:32:29.182460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.153909Z","title":"and Bassett, G","venue":null,"work_id":"607abb68-4f9d-456d-b01d-bbd8cbb8d9c1","year":1978},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.122783Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:50bed519c00f6480b9e25375e9804a16cc8aa857d0d8c398d2f7cc7235e981c9","observation_id":"44e546f5-1dad-4d1e-9b60-22d34f21186e","resolution":{"observed_at":"2026-08-14T04:32:29.159455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.06856","last_updated":"2018-03-08T11:23:13Z","snapshot_observed_at":"2026-08-14T21:10:57.213409Z","submitted_at":"2017-03-20T17:18:57Z","title":"Counterfactual Fairness","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.06856","snapshot_observed_at":"2026-08-14T04:32:28.129831Z","title":"J., Loftus, J","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.129831Z"},"links":{"cited_paper":"/paper/1703.06856","citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:94a82a5a7d3bc14bd27b9ebfdb764d03d71418566712e8746cdeecddbdf37002","observation_id":"55f5954c-b607-411b-8cd4-be7fde8a1f08","resolution":{"observed_at":"2026-08-14T04:32:28.129831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.132709Z","title":null,"venue":null,"work_id":"ebc112e3-d901-450f-8ed9-a0d8cd547dcd","year":2019},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.136234Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:c894f50feb39fde539c1df6196a80c32bae575c77bacb09d2ec6a7cf73d256e1","observation_id":"80d0804d-1992-4a3f-9675-b282d9ef816e","resolution":{"observed_at":"2026-08-14T04:32:29.139367Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.110370Z","title":null,"venue":null,"work_id":"27ce11c3-cb3a-4d82-8267-7db2d41a22be","year":2024},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.142317Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:49b478535d5482a4f4a1140a7a7c25991c5e30eeb1ed029dd6b23c158fa718e6","observation_id":"a0bd24be-6216-44cf-8354-3846ae1ffa8f","resolution":{"observed_at":"2026-08-14T04:32:29.116743Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.090582Z","title":"T., Dean, S., Rolf, E., Simchowitz, M., and Hardt, M","venue":null,"work_id":"a6d2dad5-6832-4094-9f4a-69308ba6cdaa","year":2018},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.147960Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:1999b5df95fa834dff51a4809e08bb7c30456126c9e1092bbd496cc5ad80ee7f","observation_id":"a734d2a6-c6c2-49ff-b860-77de7b28061e","resolution":{"observed_at":"2026-08-14T04:32:29.096711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.071549Z","title":null,"venue":null,"work_id":"1ad01d89-0b37-4974-8ed9-02d3819c8e21","year":2006},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.154311Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:ac94f8609fa3cd00a77056fffa4cae2a4c828b816cb6ccdd490d5af8541daf97","observation_id":"241fbc64-e571-4111-846b-5beae18f0679","resolution":{"observed_at":"2026-08-14T04:32:29.077649Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.044457Z","title":null,"venue":null,"work_id":"9d4e4edb-797f-4157-9a47-2cf4be846cae","year":2022},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.163571Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:304279ab77cef2dfa7b62d4862d14dd9196b3aba8d4ad96377c1d0aa5b01efe9","observation_id":"d4470e6e-38fe-4339-80bf-e2ba620924d3","resolution":{"observed_at":"2026-08-14T04:32:29.051470Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:29.012884Z","title":null,"venue":null,"work_id":"71d9933b-ad16-4290-8fd5-630b05590045","year":2016},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.170331Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:f08e2bf9e5f4b93e2e336abef0ccd81deae055f9ecfbcfaa749965017c24f60a","observation_id":"d6b5c338-2619-409f-ba22-b6520c3c0be6","resolution":{"observed_at":"2026-08-14T04:32:29.029108Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.991250Z","title":"C., Krishnamurthy, A., Bartlett, P., and Kakade, S","venue":null,"work_id":"c9b53d5c-cf3f-41dd-aaf5-12ac8679b55b","year":2023},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.177310Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:af134dcc00e3d327409d56fd5de3f3cb2789eb4e9b3f354a6ed597c8eb1c6245","observation_id":"e30e7557-4e6c-4433-bacd-72e525a6c535","resolution":{"observed_at":"2026-08-14T04:32:28.997577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.970593Z","title":"D., Thomas, L., Newman, S., Marinec, N., Krauss, J., Chen, J., Wu, Z., and Bohnert, A","venue":null,"work_id":"0ebab0a8-3bf4-4884-b796-18faf6f79ffa","year":2023},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.183329Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:6d4405f82880bda6f3b72ddcfbcdfc3df709980bc5b556137e2b65e20ec29c4f","observation_id":"77449e86-9bcb-43ec-916b-f2bce1f446d5","resolution":{"observed_at":"2026-08-14T04:32:28.977783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.950780Z","title":null,"venue":null,"work_id":"a454fcbf-aaf3-4b60-b688-1f5d24985e65","year":2005},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.190240Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:784f321159271d8f757ecec2d29b00529ea77db1aabcd0c12188d9929d60d365","observation_id":"4a5ec70c-d8db-46a9-8dc2-c592c806a9ab","resolution":{"observed_at":"2026-08-14T04:32:28.957278Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.931553Z","title":"and Perktold, J","venue":null,"work_id":"4405db9b-d791-4d39-814f-98a640b3e72c","year":2010},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.197363Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:59cdc81e769c605d1a0fc47ac5222be5ec74d73a69ef3e8e12b130d47349dd37","observation_id":"05f18095-e5a1-4d5c-ad20-16d7679b58c3","resolution":{"observed_at":"2026-08-14T04:32:28.937412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.911493Z","title":null,"venue":null,"work_id":"4b573dc2-087c-49aa-bbf1-d89e819f38e2","year":2000},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.206374Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:47d8a3eddd0aa3005c073ad15393c024611ad2d899ba2f41e768c399ec9450b3","observation_id":"8d962cf5-1dc0-45e9-9729-e3055dd44cd7","resolution":{"observed_at":"2026-08-14T04:32:28.918254Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06366","last_updated":"2025-01-14T04:42:08Z","snapshot_observed_at":"2026-08-16T12:59:14.392397Z","submitted_at":"2025-01-10T22:27:44Z","title":"Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06366","snapshot_observed_at":"2026-08-14T04:32:28.212392Z","title":"D., Loftus, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.212392Z"},"links":{"cited_paper":"/paper/2501.06366","citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:d9ec7a3b7ee7300aa01873b6ea34390adf3c465fbad34b2e9baac86175625f71","observation_id":"91242242-3dff-4891-ba6a-1769ed96de52","resolution":{"observed_at":"2026-08-14T04:32:28.212392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.893960Z","title":null,"venue":null,"work_id":"a716184b-da63-4504-b958-c61c3f88238a","year":2023},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.220971Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:0f71b8c4208451a76e86a7288573ec335ceb896051742459d04ad29d27bd150b","observation_id":"b403ab00-52ce-4be6-b882-83a222626564","resolution":{"observed_at":"2026-08-14T04:32:28.899392Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.871374Z","title":"offered admission","venue":null,"work_id":"5fc1459a-cddd-4db2-9352-1eeb62165687","year":2022},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.228762Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:9a3320d42514b8c80be37ceec3d938ec94fc4861aba0d36749874843dbb7dc7c","observation_id":"43a3d218-9cee-458c-a25d-c195561d50c7","resolution":{"observed_at":"2026-08-14T04:32:28.877450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.845704Z","title":"F={w T ϕ(s, a) :w∈R d,∥w∥ 1 ≤ B}, where ϕ is a feature map ˜S × A →Rd with ∥ϕ(s, a)∥∞ ≤1 and component functions {ϕi(s, a)}d i=1","venue":null,"work_id":"25a5f4c9-c3bb-4463-b947-98c3ca289b8f","year":null},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.235187Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:3f70d53f700ea2dac2c423fdd89d5efe132ce47762805305eaff0775e09934f9","observation_id":"ad520388-ddf5-47bd-a4fb-df1b6724fdc0","resolution":{"observed_at":"2026-08-14T04:32:28.852587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.821559Z","title":"LetTbe the Bellman optimality operator","venue":null,"work_id":"3e5f4a5a-f413-44f3-883c-e9249c529d0a","year":null},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.240994Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:4dc0129b35a62fa7dfe51e2feacaa89302b314bc2d9424c3eee64abf209e5664","observation_id":"6bfe92bf-1427-462d-993b-b907e76f82ae","resolution":{"observed_at":"2026-08-14T04:32:28.827924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1990.0426","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.438093Z","title":"Full”, “Unaware","venue":null,"work_id":"4614fbb2-4395-4592-bf0b-d9fee42b5417","year":2000},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.247475Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:16b4e81fd59ddb7ea243697da1e614e7091bae12c2b7fa89ddaca820460146d0","observation_id":"a55c7ff4-ceba-468c-8f2a-cdab28b2e414","resolution":{"observed_at":"2026-08-14T04:32:28.449631Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.796387Z","title":null,"venue":null,"work_id":"d48e095a-b69c-434e-a426-bab9062601ac","year":null},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.255399Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:7d323bf884cf6d9cff5752da459a1ae51ba4d7335deccabed051462c0900b1b0","observation_id":"fd4f8929-fce9-4653-843a-b6e37ac90f00","resolution":{"observed_at":"2026-08-14T04:32:28.802683Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.771522Z","title":",[ˆP(M) t,n ]−1(ˆP(M) t,n (s(M) j,t |Z=z j)|Z=z) , where ˆP(i) t,n(·|Z) is the empirical CDF of S(i) t following the same definition as that in FLAP_M","venue":null,"work_id":"1164f1ca-8382-43c1-a9c4-360e467dfeb2","year":null},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.263269Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:45385cba100ee420a1f01d49c953c95fe95270eb08d3852e5c5998b1734505d1","observation_id":"d8435553-83ae-4a32-aaf8-5d87715f7fae","resolution":{"observed_at":"2026-08-14T04:32:28.778039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.749001Z","title":null,"venue":null,"work_id":"ce17f4b8-5215-48ac-96ca-df7208d7053e","year":null},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.271250Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:4d4469967035a8a0a6936ad545a61229ddd9f473644f281a4a72577aea1c60d5","observation_id":"cc72fa4f-9a04-4e8b-b558-6fb13c6e12bf","resolution":{"observed_at":"2026-08-14T04:32:28.755885Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:32:28.719887Z","title":"Implementation of CFSDP:Our implementation of CFSDP estimates the environment’s transition kernel using a neural network with hidden layers [64,64]","venue":null,"work_id":"520666c1-cd17-4510-bf32-d26cd70ba20f","year":2000},"citing_paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T04:32:28.279296Z"},"links":{"citing_paper":"/paper/2608.08743"},"observation_digest":"sha256:27cf636aeeb89e87c14fc02a006388a2be1bcabcc9f4e4f48cc5d22b99129cb7","observation_id":"2abffb74-8b83-4170-976b-e657d2f7e5e6","resolution":{"observed_at":"2026-08-14T04:32:28.729754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.08743","last_updated":"2026-08-09T14:42:47Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-18T03:15:44.860509Z","submitted_at":"2026-08-09T14:42:47Z","title":"A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":2,"verified_fuzzy":15},"total_outbound_references":40},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.08743."}