{"as_of":"2026-08-14T09:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:17f41fea4f877ef020ff0b68d91a46cb97e26f15127c2c57b59117a0a9aff9a0","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":"2026-08-14T06:32:32.682623+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":"2026-08-11T15:48:52.367061Z","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":"2026-07-04T12:29:52.055883Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2208.12584","last_updated":"2023-02-03T11:32:23Z","snapshot_observed_at":"2026-08-13T14:38:30.121490Z","submitted_at":"2022-08-26T11:01:55Z","title":"Socially Fair Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.12584","snapshot_observed_at":"2026-08-11T15:48:52.367061Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.10751","last_updated":"2024-12-14T08:38:26Z","snapshot_observed_at":"2026-08-14T06:56:03.299746Z","submitted_at":"2024-12-14T08:38:26Z","title":"p-Mean Regret for Stochastic Bandits","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T15:48:52.367061Z"},"links":{"cited_paper":"/paper/2208.12584","citing_paper":"/paper/2412.10751"},"observation_digest":"sha256:79369fcd17a11a883368c219a4395a5356bd9befbedd69d67c97c4731f1c2108","observation_id":"9cae0340-d051-47f5-a34d-f4eeeb3f4ca1","resolution":{"observed_at":"2026-08-11T15:48:52.367061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.12584","last_updated":"2023-02-03T11:32:23Z","snapshot_observed_at":"2026-08-13T14:38:30.121490Z","submitted_at":"2022-08-26T11:01:55Z","title":"Socially Fair Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.12584","snapshot_observed_at":"2026-08-11T05:52:34.548025Z","title":"Socially fair reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.17123","last_updated":"2024-12-31T14:55:25Z","snapshot_observed_at":"2026-08-13T18:18:49.902453Z","submitted_at":"2024-12-22T18:23:06Z","title":"Fairness in Reinforcement Learning with Bisimulation Metrics","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T05:52:34.548025Z"},"links":{"cited_paper":"/paper/2208.12584","citing_paper":"/paper/2412.17123"},"observation_digest":"sha256:96b0ff862c3ecafa81f4c4f3ff4a6ea3ec6cfefee22c73bfca7f170bd94889e4","observation_id":"cca406cb-314b-4baf-b432-62cb5f4153e4","resolution":{"observed_at":"2026-08-11T05:52:34.548025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.12584","last_updated":"2023-02-03T11:32:23Z","snapshot_observed_at":"2026-08-13T14:38:30.121490Z","submitted_at":"2022-08-26T11:01:55Z","title":"Socially Fair Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2208.12584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2208.12584","snapshot_observed_at":"2026-07-04T12:29:52.055883Z","title":"arXiv preprint arXiv:2208.12584 , year=","venue":null,"work_id":"c85313a1-1fff-44d1-a744-1953182b7870","year":2023},"citing_paper":{"arxiv_id":"2605.01961","last_updated":"2026-05-03T16:47:24Z","snapshot_observed_at":"2026-08-11T02:03:12.562167Z","submitted_at":"2026-05-03T16:47:24Z","title":"Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-10T15:06:03.751052Z"},"links":{"cited_paper":"/paper/2208.12584","citing_paper":"/paper/2605.01961"},"observation_digest":"sha256:6ea5e7f5f67799d01f904785dfa02f1b771a58a40ab8abc9f139bae433f18ff4","observation_id":"8a870e05-a123-40e1-ad4d-9e1d458f7817","resolution":{"observed_at":"2026-05-11T11:11:06.360159Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.12584","last_updated":"2023-02-03T11:32:23Z","snapshot_observed_at":"2026-08-13T14:38:30.121490Z","submitted_at":"2022-08-26T11:01:55Z","title":"Socially Fair Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2208.12584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2208.12584","snapshot_observed_at":"2026-07-04T12:29:52.055883Z","title":"arXiv preprint arXiv:2208.12584 , year=","venue":null,"work_id":"c85313a1-1fff-44d1-a744-1953182b7870","year":2023},"citing_paper":{"arxiv_id":"2606.18111","last_updated":"2026-06-16T16:16:54Z","snapshot_observed_at":"2026-08-05T13:40:52.360184Z","submitted_at":"2026-06-16T16:16:54Z","title":"Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T00:58:29.900902Z"},"links":{"cited_paper":"/paper/2208.12584","citing_paper":"/paper/2606.18111"},"observation_digest":"sha256:a98e3ba8249c6df73117bebfdc339c5d475b7ec81eae9f80c0336a01ff4c40d3","observation_id":"8ba50916-e4d7-4757-9530-79e66ab7eb87","resolution":{"observed_at":"2026-07-03T20:58:58.452809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.12584","last_updated":"2023-02-03T11:32:23Z","snapshot_observed_at":"2026-08-13T14:38:30.121490Z","submitted_at":"2022-08-26T11:01:55Z","title":"Socially Fair Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2208.12584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2208.12584","snapshot_observed_at":"2026-07-04T12:29:52.055883Z","title":"arXiv preprint arXiv:2208.12584 , year=","venue":null,"work_id":"c85313a1-1fff-44d1-a744-1953182b7870","year":2023},"citing_paper":{"arxiv_id":"2606.23931","last_updated":"2026-06-22T20:50:45Z","snapshot_observed_at":"2026-08-07T10:23:46.507640Z","submitted_at":"2026-06-22T20:50:45Z","title":"Welfarist Control Design -- How to fulfill the societal mandate in multi-agent control?","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-06-26T06:44:15.559910Z"},"links":{"cited_paper":"/paper/2208.12584","citing_paper":"/paper/2606.23931"},"observation_digest":"sha256:2355cd32e0a53a932d4ef55b5e29ccdba58e31bb0d567cd4e45bd9d98eafca61","observation_id":"385d0b49-f910-4ce8-ac99-6a870df8a9dd","resolution":{"observed_at":"2026-07-04T12:29:52.060586Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2208.12584/citation-record","integrity":"/paper/2208.12584/integrity","json":"/paper/2208.12584/citation-record.json","paper":"/paper/2208.12584"},"outbound":[],"paper":{"arxiv_id":"2208.12584","last_updated":"2023-02-03T11:32:23Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T14:38:30.121490Z","submitted_at":"2022-08-26T11:01:55Z","title":"Socially Fair 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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2208.12584."}