{"as_of":"2026-08-16T23:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d023a750c67527a82a8d87d8e0893458315254f79ff0aed31fe1cefea08591e8","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:40:45.727688Z","state":"measured"},{"denominator":63,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":63,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-01T01:40:08.230114Z","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":"2026-07-01T12:55:44.116744Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"cited_work":{"arxiv_id":"2412.14002","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.14002","snapshot_observed_at":"2026-07-01T12:55:44.116744Z","title":"Operator splitting for convex constrained Markov decision processes,","venue":null,"work_id":"0a81b058-4bea-4654-9b87-5aab8938a94d","year":2024},"citing_paper":{"arxiv_id":"2606.30829","last_updated":"2026-06-29T19:00:01Z","snapshot_observed_at":"2026-08-09T01:11:24.442182Z","submitted_at":"2026-06-29T19:00:01Z","title":"Joint Chance Constrained Safe-Optimal Control","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-01T01:40:08.230114Z"},"links":{"cited_paper":"/paper/2412.14002","citing_paper":"/paper/2606.30829"},"observation_digest":"sha256:0f1a21e7a18a362df6be594dd94a4cd1706e6f6e1ea4d272addb23e5fd20a083","observation_id":"c91b5c64-12f6-42f0-b676-61bc626b43da","resolution":{"observed_at":"2026-07-01T12:55:44.118969Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.14002/citation-record","integrity":"/paper/2412.14002/integrity","json":"/paper/2412.14002/citation-record.json","paper":"/paper/2412.14002"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:46.471014Z","title":"Mastering the game of go without human knowledge,","venue":null,"work_id":"520b4890-a5e7-4820-a255-7574408d0a89","year":2017},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.523266Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:094843abefd28cec6923b522770a2c647977b8899ce40c1abe24ef14f0cdf617","observation_id":"7d5debb6-f0c9-42d9-8d71-e9b5c137e1ba","resolution":{"observed_at":"2026-08-11T12:40:46.474791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.459939Z","title":"Magnetic control of tokamak plasmas through deep reinforcement learning,","venue":null,"work_id":"6331b97b-b84f-40dc-9800-3176a74739c4","year":2022},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.527737Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:2366e705c86d1dbd4cc754b8a2c45e6d5d365ed71111fc39be076120a25de6c2","observation_id":"a14a017c-7b4c-46bb-93e6-842395d858bc","resolution":{"observed_at":"2026-08-11T12:40:46.463803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.531734Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.531734Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:497f3ac960992632756ffd1977633efbb5879b25d6c520aff720cd4012ced91b","observation_id":"fc2e3662-6eba-4b80-8a36-fd86e8f01db8","resolution":{"observed_at":"2026-08-11T12:40:45.531734Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.535342Z","title":"Altman, Constrained Markov decision processes","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.535342Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:7795146e4a0ff8a1b23f86e8243e670ded75f7802f21eea5d5bf7ca6771f2175","observation_id":"d5ac92ef-5561-4ba3-9ad0-9402d472a519","resolution":{"observed_at":"2026-08-11T12:40:45.535342Z","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":"2026-08-11T12:40:46.433777Z","title":"Policy gradients with variance related risk criteria,","venue":null,"work_id":"77f42ef1-2e63-4807-b368-1056d26b9db9","year":2012},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.539032Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:cfa7cbe3ef6fbe72d049faa541185489d7133a9d0d7385014fea57df58d0d57b","observation_id":"5a515a8e-f66a-489c-a58b-7e289c4e5f3f","resolution":{"observed_at":"2026-08-11T12:40:46.438344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.422915Z","title":"Risk-constrained reinforcement learning with percentile risk criteria,","venue":null,"work_id":"3f33d1cd-16b4-4268-921b-742b45a2dabd","year":2018},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.542889Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:7c11ef20d3835df3f4a6c30bb00019ae465ee483bd2f3711f6b589c989aec01c","observation_id":"9ec6acb3-8b02-4766-91c3-e51e28f664db","resolution":{"observed_at":"2026-08-11T12:40:46.426469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.412399Z","title":"Control and optimization meet the smart power grid: Scheduling of power demands for optimal energy management,","venue":null,"work_id":"4bb8aaeb-a82a-4596-b94a-c06b48ec3087","year":2011},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.546898Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:e750e35bb9841b0b63a7203c3619407cb97cb77014df6c469d29bd3fc01854fd","observation_id":"ee91af8a-5923-494f-a011-6aca5f6ab9d7","resolution":{"observed_at":"2026-08-11T12:40:46.416377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.402799Z","title":"Constrained policy optimization,","venue":null,"work_id":"b5f526c5-4dbe-4bde-b2c6-0b38f671e423","year":2017},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.550549Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:0e0dd7cfa3d6998f0c274f07f9dbf03271b2e2005fd64e7216e90b9153267d86","observation_id":"542b87d3-b08d-435d-be6e-0e07797a0d49","resolution":{"observed_at":"2026-08-11T12:40:46.406379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.393030Z","title":"Dynamic programming equations for dis- counted constrained stochastic control,","venue":null,"work_id":"d97de42e-2497-4bca-8d1d-7f1f4c376251","year":2004},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.553944Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:76ea90a01f723d7a84f158ed13bcfa81c287db1a0ce37ecb1a864986dba76dcc","observation_id":"45b38f5c-3b29-440f-81cb-573b57a0950d","resolution":{"observed_at":"2026-08-11T12:40:46.396802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.383744Z","title":"Dynamic programming in constrained Markov decision processes,","venue":null,"work_id":"af0752c1-ff80-4b5b-aeec-5a1e51deef20","year":2006},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.557316Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:06764cb88fd9c2c74141e9c191ce506a49840a3fa806ffa1476a8faa9167a333","observation_id":"ad5d1b4d-e489-46fd-b2bf-0ace75ad2016","resolution":{"observed_at":"2026-08-11T12:40:46.387474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.03718","last_updated":"2020-05-07T19:38:09Z","snapshot_observed_at":"2026-08-15T10:15:54.217577Z","submitted_at":"2020-05-07T19:38:09Z","title":"A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes","version":1},"cited_work":{"arxiv_id":"2005.03718","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.03718","snapshot_observed_at":"2026-08-11T12:40:45.938102Z","title":"A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes","venue":"cs.LG","work_id":"b9136a27-6c75-41e6-b36a-ed481733ffa8","year":2020},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.560934Z"},"links":{"cited_paper":"/paper/2005.03718","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:d5f0da1e38690430c10b9f8e475e8c41ebee70141b4909a3a50503b7eb3552b8","observation_id":"cd8afd8c-185d-45f4-bb8d-cdd29e24ae5f","resolution":{"observed_at":"2026-08-11T12:40:45.942537Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.372968Z","title":"Natural policy gradient primal-dual method for constrained Markov decision processes,","venue":null,"work_id":"2193f14a-be44-4940-b1c3-b529c288d2a9","year":2020},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.564782Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:481fc96dbb8ab82919782c64a7ea3fd010f7439c60cf1dc398452bb374c6b37c","observation_id":"81bb3222-c4a7-4975-9bfa-60d1a820a59d","resolution":{"observed_at":"2026-08-11T12:40:46.376767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.363368Z","title":"Learning policies with zero or bounded constraint violation for constrained MDPs,","venue":null,"work_id":"58e6c4a3-40bd-4d0a-80ab-9fe26fd2facd","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.568209Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:fe25c6f1923503d9984ac0ed0639858f0ccf929ed7b12bb0a5d9908cb2df096d","observation_id":"a42db668-8b00-4eca-88ab-0631211d5210","resolution":{"observed_at":"2026-08-11T12:40:46.366953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.11941","last_updated":"2023-09-21T14:36:25Z","snapshot_observed_at":"2026-08-16T18:43:30.172901Z","submitted_at":"2021-02-23T21:07:35Z","title":"State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards","version":2},"cited_work":{"arxiv_id":"2102.11941","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.11941","snapshot_observed_at":"2026-08-11T12:40:45.923824Z","title":"State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards","venue":"cs.LG","work_id":"439809a0-a9f7-4e51-b16d-587edbbcb24b","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.571142Z"},"links":{"cited_paper":"/paper/2102.11941","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:c31b061fc651b763ddb724e4447cbe042c7621ad43ead68159b10fdeef863aa9","observation_id":"e5e4fd73-681f-4d25-8067-da8a5f2ce359","resolution":{"observed_at":"2026-08-11T12:40:45.928287Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.353201Z","title":"Constrained MDPs and the reward hypothesis","venue":null,"work_id":"114006c5-fb72-4bfe-ac0a-b5de1bb00763","year":2020},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.574448Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:e3e59cf605bf134556f9d4da659e62432a62a8e97afcad8dbb714ed66a2f18d1","observation_id":"059a8990-aadc-44e1-b9e0-bbe49e2e94f9","resolution":{"observed_at":"2026-08-11T12:40:46.357018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.342980Z","title":"Two “well-known","venue":null,"work_id":"6eb4f180-8409-4c90-8d4f-b6dffff750bc","year":2009},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.576948Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:741fa6e87057ae2722df0632207f85faf8419e9f8de2ff72af4bf6b31956c6bb","observation_id":"dc886c82-2731-4f9d-a3a9-9c3e88d9d4ec","resolution":{"observed_at":"2026-08-11T12:40:46.346626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.332186Z","title":"Algorithm for constrained Markov decision process with linear convergence,","venue":null,"work_id":"931923f0-6457-4729-8fa3-791081de27a0","year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.579723Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:f5035dbf3edd4480f6ba4764b637578052ebc0d5c6dffdfe840c4b08b17f92de","observation_id":"94358415-df0b-4c2c-8470-c8a9c5a99ca8","resolution":{"observed_at":"2026-08-11T12:40:46.336444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.10351","last_updated":"2021-10-20T02:57:21Z","snapshot_observed_at":"2026-08-16T17:47:59.937602Z","submitted_at":"2021-10-20T02:57:21Z","title":"Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process","version":1},"cited_work":{"arxiv_id":"2110.10351","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.10351","snapshot_observed_at":"2026-08-11T12:40:45.908610Z","title":"Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process","venue":"math.OC","work_id":"11e1b172-bdfb-4038-aa45-72afcdc420f5","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.582528Z"},"links":{"cited_paper":"/paper/2110.10351","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:5dbf7751d8be41b10e5be194348c0756d7e5130c5df770c08378b60affe76f0a","observation_id":"df37e178-a510-4759-9e23-c8ca8325bc49","resolution":{"observed_at":"2026-08-11T12:40:45.912662Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.07001","last_updated":"2023-08-30T15:58:45Z","snapshot_observed_at":"2026-08-16T15:24:39.325878Z","submitted_at":"2023-06-12T10:10:57Z","title":"Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.07001","snapshot_observed_at":"2026-08-11T12:40:45.585747Z","title":"Cancellation-free regret bounds for lagrangian approaches in constrained Markov decision processes,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.585747Z"},"links":{"cited_paper":"/paper/2306.07001","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:93d3b5fa47e4a723ab41191a858ebb68fdf436ef9c16b25449e733246dfbf42f","observation_id":"436de9d0-9ff3-4e8f-8b7a-e28e86f689d4","resolution":{"observed_at":"2026-08-11T12:40:45.585747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11700","last_updated":"2024-01-17T04:52:39Z","snapshot_observed_at":"2026-08-16T15:22:28.361972Z","submitted_at":"2023-06-20T17:27:31Z","title":"Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11700","snapshot_observed_at":"2026-08-11T12:40:45.589096Z","title":"Last-iterate convergent policy gradient primal-dual methods for constrained MDPs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.589096Z"},"links":{"cited_paper":"/paper/2306.11700","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:b7c76d9ef6a46a6f2a06455fc50b9072409175a28f653cd975489824e090b4f8","observation_id":"e7dc5fbf-0b77-45e9-bc34-8fb93dbf9c2d","resolution":{"observed_at":"2026-08-11T12:40:45.589096Z","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":"2026-08-11T12:40:46.320217Z","title":"Reload: Reinforcement learning with optimistic ascent- descent for last-iterate convergence in constrained MDPs,","venue":null,"work_id":"64063f2f-1184-472e-8eea-66704c562d20","year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.592853Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:e576d4bdcbd44a87fc0f3358ea1df3f8c73dcc70042ff360cc88ad11d9418885","observation_id":"d9b00ec9-7e4a-45d9-b6c9-35dac966afef","resolution":{"observed_at":"2026-08-11T12:40:46.324278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.309460Z","title":"Ipo: Interior-point policy optimization under constraints,","venue":null,"work_id":"4e074bcd-0292-42da-84d7-844d8dea33f3","year":2020},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.596272Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:f93fd4d7b3a327b07af139b9948a1f8c1c9452546d756ab846c13da815d6f611","observation_id":"15e44324-8a48-4e21-aec3-1d4ff6d76fea","resolution":{"observed_at":"2026-08-11T12:40:46.313087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03152","last_updated":"2020-10-07T04:22:45Z","snapshot_observed_at":"2026-08-16T19:15:19.794542Z","submitted_at":"2020-10-07T04:22:45Z","title":"Projection-Based Constrained Policy Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.03152","snapshot_observed_at":"2026-08-11T12:40:45.599554Z","title":"Projection- based constrained policy optimization,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.599554Z"},"links":{"cited_paper":"/paper/2010.03152","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:8e714b424a71dfb025f91dfdf1a61b07cca5692b694e42c5529c95ed2397fadf","observation_id":"d9ca5f64-4792-4ad5-8b81-cca65e75d970","resolution":{"observed_at":"2026-08-11T12:40:45.599554Z","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":"2026-08-11T12:40:46.299170Z","title":"Reward is enough for convex MDPs,","venue":null,"work_id":"23012c2c-ec7e-45b8-a4f3-9c2a9fae675f","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.604198Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:d32d67e2b33bcb0419461d4c6cc3781d639fca58da351304ed603180e53aa9c2","observation_id":"0bc2fc3e-f15d-42a0-b7ac-9ed2ce28805e","resolution":{"observed_at":"2026-08-11T12:40:46.302622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.288643Z","title":"Apprenticeship learning via inverse reinforce- ment learning,","venue":null,"work_id":"a7a5ccb8-028f-4d94-9ef4-c3ce338adccf","year":2004},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.607868Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:ff4035beb122582a91bbd7844f33d41a56c203857dbc6243b8cb3522fc705617","observation_id":"3b81aac5-0a43-47d3-8586-bddbc21ce90a","resolution":{"observed_at":"2026-08-11T12:40:46.292322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.278111Z","title":"Provably efficient maximum entropy exploration,","venue":null,"work_id":"609d8f14-8e72-4168-927d-1e89a96abfbe","year":2019},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.611007Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:64cd9b680ce8e30174a800e46fedc7d24d00f8156477389523b306c61ee46306","observation_id":"41a27b59-5d10-424e-9af1-3f570162db0b","resolution":{"observed_at":"2026-08-11T12:40:46.281858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06070","last_updated":"2018-10-09T23:19:52Z","snapshot_observed_at":"2026-08-14T19:45:07.867570Z","submitted_at":"2018-02-16T18:57:57Z","title":"Diversity is All You Need: Learning Skills without a Reward Function","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06070","snapshot_observed_at":"2026-08-11T12:40:45.614529Z","title":"Diversity is all you need: Learning skills without a reward function,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.614529Z"},"links":{"cited_paper":"/paper/1802.06070","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:5b549d96d0e5b34ab310075d9c9f8f82468eb018361d22f1f9e491e3677c37da","observation_id":"b9945c27-18a9-4bb5-8a98-148d484fa784","resolution":{"observed_at":"2026-08-11T12:40:45.614529Z","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":"2026-08-11T12:40:46.268063Z","title":"Policy-based primal-dual methods for convex constrained Markov decision processes,","venue":null,"work_id":"8008a31d-bb38-4d39-bec0-b624317d4ed5","year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.618422Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:a4591bf0422b4c8d53d5226bbb4db28d5c323179cf4f8e639773a1c2cac43cb0","observation_id":"a405dd85-26bc-424e-9b46-9cece872a9ad","resolution":{"observed_at":"2026-08-11T12:40:46.271500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.256982Z","title":"Variational policy gradient method for reinforcement learning with general utilities,","venue":null,"work_id":"288092a7-865e-42b4-8a7c-6a61ea16b68b","year":2020},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.621795Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:2e86cff5dad3393d0478bad191e342fdacafc3f890487f0bcb784b13d06b5899","observation_id":"5d238fcc-519d-47b8-97fe-741bdfe0b226","resolution":{"observed_at":"2026-08-11T12:40:46.260720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.245699Z","title":"Reinforcement learning with convex constraints,","venue":null,"work_id":"d06d5ab7-0aac-4870-a414-fbf12e4b6a75","year":2019},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.625100Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:b1273bb7544dad65220ed5ff072a3a39a0a3bd607118eab372c905d3afc3f0a4","observation_id":"5c4a64fb-80a3-4e21-babd-fb34144bf916","resolution":{"observed_at":"2026-08-11T12:40:46.249377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.234537Z","title":"A simple reward-free approach to constrained reinforcement learning,","venue":null,"work_id":"3973c735-d2ce-4c76-b0d2-e9fb3c9f4dd7","year":2022},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.628643Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:6d2d1455a8c32c165b10787ecfcfee31369f83109eee3e981d138dc9f59db458","observation_id":"6452146a-e617-4068-b5a7-af6bf91dbf54","resolution":{"observed_at":"2026-08-11T12:40:46.238752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.223821Z","title":"Bauschke and P","venue":null,"work_id":"25dc020e-3e7b-4022-994d-dd1693da6b9a","year":2017},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.632070Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:3abcbf15b2db448c6a43393ef71602aded048a0cf743a68732dffcfc2f58a3a7","observation_id":"002b4718-6274-4d5d-8a35-5130c8ea7d9c","resolution":{"observed_at":"2026-08-11T12:40:46.227584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.635420Z","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.635420Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:a72cb6bed6189dafdc63b1098e3d2aaf88ef1d9aacfc155807e155407f116cf3","observation_id":"aae25587-1f49-4ccd-bdf5-ae98d45aed63","resolution":{"observed_at":"2026-08-11T12:40:45.635420Z","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":"2026-08-11T12:40:46.205514Z","title":"A note on the equivalence of operator splitting methods,","venue":null,"work_id":"f5fbb1a2-3a72-40dc-a6cd-7b92db0e57f0","year":2019},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.638587Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:6f384dd7ed7f5ce9136040b59498b1e158102077cfe153ee5b597542dc969eb2","observation_id":"8694d196-c0b1-433a-91ec-0f717a1e6517","resolution":{"observed_at":"2026-08-11T12:40:46.210336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.194846Z","title":"Provably efficient algorithms for multi-objective competitive RL,","venue":null,"work_id":"551a469e-3a4f-41ce-a831-dcb0950055a4","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.641928Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:17891351e23c1f3d16ed8af02462bb2ca398d31c5699022e0de56b2ce43ecfba","observation_id":"729dc1db-fde2-477b-9cc8-c21276720462","resolution":{"observed_at":"2026-08-11T12:40:46.198587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.183810Z","title":"A splitting method for optimal control,","venue":null,"work_id":"b1a6e372-c2f0-4f5b-9c6d-c8d32d644b23","year":2013},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.645327Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:57ec4c18fe05d457b66cf79f3c576b926139d75688b94a1ac3b1f05ff2dd0f5b","observation_id":"148017e3-2317-4d51-86ef-164c340ffebb","resolution":{"observed_at":"2026-08-11T12:40:46.187573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07798","last_updated":"2017-05-22T15:06:25Z","snapshot_observed_at":"2026-08-14T20:59:03.934741Z","submitted_at":"2017-05-22T15:06:25Z","title":"A unified view of entropy-regularized Markov decision processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07798","snapshot_observed_at":"2026-08-11T12:40:45.648460Z","title":"A unified view of entropy-regularized Markov decision processes,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.648460Z"},"links":{"cited_paper":"/paper/1705.07798","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:f5836919e4af8eb8dc39b4e8a7dfd445eec9195e647a1e973c9b8bbad7996160","observation_id":"335a47a6-b86e-4328-95d5-b970d8ec748d","resolution":{"observed_at":"2026-08-11T12:40:45.648460Z","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":"2026-08-11T12:40:46.173256Z","title":"A theory of regularized Markov decision processes,","venue":null,"work_id":"fae61154-e641-4254-990b-584a5f182ee2","year":2019},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.651895Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:8323360220a1c2452f63e23425693b2653446369c8d71fa6af119ef84d298aef","observation_id":"34500308-5375-46e9-a658-cf89b33ce04e","resolution":{"observed_at":"2026-08-11T12:40:46.176808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.161784Z","title":"Dynamic programming through the lens of semismooth Newton-type methods,","venue":null,"work_id":"3fd92199-b37f-44b7-9e96-7ba2edcd97c3","year":2022},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.655087Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:4f09d4e551dbf04935e62b45e28223da4bf3966aea3708be8834239a65e431a8","observation_id":"47314e45-c403-49b2-9885-921c406adbef","resolution":{"observed_at":"2026-08-11T12:40:46.165676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.658357Z","title":"From optimization to control: quasi policy iteration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.658357Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:6efacd27b3481f148c7a2364c1492bcbc73c52f0c89844ce0b3f5ffcea21ca49","observation_id":"f226bed8-e293-433e-ae8c-f2f142ea9ed2","resolution":{"observed_at":"2026-08-11T12:40:45.658357Z","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":"2026-08-11T12:40:46.150649Z","title":"On the minimal displacement vector of the Douglas–Rachford operator,","venue":null,"work_id":"13072685-7857-4901-8709-930065aa3f2c","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.661823Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:0bf0867517e45aaf66951c1bb12853bb4a9451ce62549178071a44f68fd2ba34","observation_id":"c85f91e9-7369-41ab-9e12-03177f016048","resolution":{"observed_at":"2026-08-11T12:40:46.154673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.140404Z","title":"On the Douglas–Rachford algorithm for solving possibly inconsistent optimization problems,","venue":null,"work_id":"2ffeba3c-b24f-4d3f-8bc2-8833fdc2ab85","year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.664885Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:2d64c5888637374b53eb127c9e5b7cc76e838675b4e6259da52d62fc64f6582f","observation_id":"845b7a3d-417a-45ab-a633-b0261fba5ace","resolution":{"observed_at":"2026-08-11T12:40:46.143746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.130248Z","title":"Infeasibility detection in alternating direction method of multipliers for convex quadratic programs,","venue":null,"work_id":"f5030625-211a-42b5-8b2f-94212ba67fed","year":2014},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.668212Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:101e012e55098378be1fec04ba544f1b48c80d7fd0dcfd82e38b1a0ac2be4b89","observation_id":"d1af1847-d264-4ecb-b59f-461b404f105a","resolution":{"observed_at":"2026-08-11T12:40:46.133546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02374","last_updated":"2017-10-15T17:54:28Z","snapshot_observed_at":"2026-08-14T20:55:25.935523Z","submitted_at":"2017-06-07T20:35:49Z","title":"A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs","version":3},"cited_work":{"arxiv_id":"1706.02374","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.02374","snapshot_observed_at":"2026-08-11T12:40:45.768899Z","title":"A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs","venue":"math.OC","work_id":"a14c0fd6-d234-45f9-8647-26734cf7c043","year":2017},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.672091Z"},"links":{"cited_paper":"/paper/1706.02374","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:33aef1a12626c6d3831fbe75d91cf016dc39d419ad16b183f5fe6dee2b3821dc","observation_id":"38476989-5207-4d52-9083-c9d5b7edbc04","resolution":{"observed_at":"2026-08-11T12:40:45.774770Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.117944Z","title":"Infeasibility detection in the alternating direction method of multipliers for convex optimization,","venue":null,"work_id":"06abf527-4ba8-4b94-8bd9-34c2c71162f2","year":2019},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.675613Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:3affc67d91c6e81b315fd03dfc0382fe32cb3809e33232cc07ee0574e6fe5b49","observation_id":"c19741f1-0603-4eff-8ff8-a0f75088959b","resolution":{"observed_at":"2026-08-11T12:40:46.122245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.104596Z","title":null,"venue":null,"work_id":"44c9429c-3de7-4623-8841-92243b420688","year":2009},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.678725Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:b0bdc1be2a1573979f6ab9e6a234ad185e96aa27ef83db50f66b9bf54476d00d","observation_id":"2d88bf23-f813-4fe2-943b-38f62e751c1b","resolution":{"observed_at":"2026-08-11T12:40:46.108724Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.093063Z","title":"On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators,","venue":null,"work_id":"29ef8189-e34d-42f8-99ef-d88d587fc376","year":1992},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.681633Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:e2af60f2d0604292a270a4afa0602515a0fc799f40bcec637909463e65abf857","observation_id":"1b6545ba-014c-4585-a7dd-adf545063e20","resolution":{"observed_at":"2026-08-11T12:40:46.096953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.080311Z","title":"On the convergence of the coordinate descent method for convex differentiable minimization,","venue":null,"work_id":"b7a96249-5512-4527-a3e8-236958604458","year":1992},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.684600Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:1107875be09e9a57b50b40891251e411cebcb7041b49b91c94b96b5bbf1c61cd","observation_id":"c69d66ec-44ed-4ae4-aa7c-882db7784a41","resolution":{"observed_at":"2026-08-11T12:40:46.084852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.068439Z","title":"Nocedal and S","venue":null,"work_id":"07c552ed-4c95-4eb3-ab73-86c3f76a25b7","year":2006},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.687334Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:6cb688124260690a3a1a04b4d1ef880ab0d4373e8b8476433438a5cb9e18c50f","observation_id":"b49bbd16-32d3-4d0c-a4da-e95a873a9931","resolution":{"observed_at":"2026-08-11T12:40:46.072601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.056487Z","title":"Operator-splitting methods for monotone affine variational inequalities, with a parallel application to optimal control,","venue":null,"work_id":"ef5c15f1-3614-41f5-a314-b800db922999","year":1998},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.690317Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:015d46772254a256877bf3c7d19d0a02457a3a44516c0c880eca3b1304360e86","observation_id":"9e4d7071-9aac-4c60-99b2-5ec01d98fb67","resolution":{"observed_at":"2026-08-11T12:40:46.060999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.693417Z","title":"Parallel alternating direction multiplier decomposition of convex programs,","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.693417Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:469c392880128248a4a7ea41f4051873938f18ba4f2d32984ac1c72d63ad37ae","observation_id":"a1b6c6ae-34c2-4999-90b9-0e8a26dfd4b6","resolution":{"observed_at":"2026-08-11T12:40:45.693417Z","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":"2026-08-11T12:40:46.037307Z","title":"Natural Actor- Critic Algorithms,","venue":null,"work_id":"48b6ce10-c603-4c96-b5ac-578740ed8638","year":2009},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.696698Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:eff9a3d965527f3728f6b961fa53140dcb6c98f34a9b795a832ad226c96e7127","observation_id":"5dbb4e01-7ed9-4674-8b68-5ef2c559d5f2","resolution":{"observed_at":"2026-08-11T12:40:46.041883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.699652Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.699652Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:0e06258a94241bd00f3f7a2fc7e58a6a1f940e64d0dc3e6a5f32a4c918122425","observation_id":"c93d3625-2ce4-4e7d-a5b2-e990d6dbc021","resolution":{"observed_at":"2026-08-11T12:40:45.699652Z","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":"2026-08-11T12:40:46.018595Z","title":"PID accelerated value iteration algorithm,","venue":null,"work_id":"3f21b307-f515-49ad-a3bf-89f442b07f65","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.702427Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:f02eafe4b42bb721ed3619242ba4be504f521f648f6c0a2e97dd20ad5ac34354","observation_id":"88f42b45-ae06-4a60-8f92-525e4980a090","resolution":{"observed_at":"2026-08-11T12:40:46.022465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:46.004805Z","title":"Scalable first-order methods for robust MDPs,","venue":null,"work_id":"b346cd3d-f960-42f9-8a6e-7682566c7b6f","year":2021},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.705410Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:b07d86614f6d30baf090945181187eada4778abceddab9351d9130fd268d10a2","observation_id":"e5599083-65e5-438d-9d0e-c179ab9b09e5","resolution":{"observed_at":"2026-08-11T12:40:46.008711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:45.991768Z","title":"Integrating a partial model into model free reinforcement learning.,","venue":null,"work_id":"574477bc-9f89-4de9-8d67-e81ac2f92bc8","year":2012},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.708307Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:87d4f0f653f6f68ed886dae0e784f1796faf2714646c395924bb364c1cd225a6","observation_id":"39b06f7f-f487-4906-9d07-fdd4f6b374f1","resolution":{"observed_at":"2026-08-11T12:40:45.996527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.711404Z","title":"Gurobi Optimizer Reference Manual,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.711404Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:a69dcec3c89bb9a96a9118e3463b6b22a010ca1b5c5207949a2a2d68b64543e8","observation_id":"63fdc30f-6f3c-4af2-a99d-59108ef8521e","resolution":{"observed_at":"2026-08-11T12:40:45.711404Z","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":"2026-08-11T12:40:45.974926Z","title":"Safe policies for reinforcement learning via primal-dual methods,","venue":null,"work_id":"63488fb9-88b3-4cac-8fc2-6b586e94e9ef","year":2022},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.714654Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:869300e67c13d9cc8d583054c9f9981da994cb56d387f46c929fe11c98fd2ccf","observation_id":"259e0a59-fbdb-4c88-a3c2-6483e6ec897c","resolution":{"observed_at":"2026-08-11T12:40:45.978868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T12:40:45.964586Z","title":"Conic optimization via operator splitting and homogeneous self-dual embedding,","venue":null,"work_id":"7fe676df-b95e-41f0-8bb5-cc15e0ad1649","year":2016},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.718057Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:ce3fd5b583f0343a14e798ff03529a708c2882b883734734e85265b7c905ee3e","observation_id":"2729d892-f1e5-4e18-b5ff-3355062fa1bd","resolution":{"observed_at":"2026-08-11T12:40:45.968594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.11074","last_updated":"2018-12-26T11:09:40Z","snapshot_observed_at":"2026-08-14T19:10:15.765536Z","submitted_at":"2018-05-28T17:31:11Z","title":"Reward Constrained Policy Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.11074","snapshot_observed_at":"2026-08-11T12:40:45.721211Z","title":"Reward constrained policy optimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.721211Z"},"links":{"cited_paper":"/paper/1805.11074","citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:e385b2905f65b3f0718e6884683ea18dd99c1b6cb75330c141fe1f2c9d3959c1","observation_id":"d61bd2c1-8f1d-44a1-b1e3-8e9084a80edd","resolution":{"observed_at":"2026-08-11T12:40:45.721211Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.724621Z","title":"Markov decision processes,","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.724621Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:2fc641b1bd6578972d7738e4ba6379b8911957dcf1ae6977a113c400c5030c42","observation_id":"992f0858-c3e3-459b-ab05-cc8006d66e68","resolution":{"observed_at":"2026-08-11T12:40:45.724621Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:40:45.727688Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T12:40:45.727688Z"},"links":{"citing_paper":"/paper/2412.14002"},"observation_digest":"sha256:9ffd193b4e380fc9597210a3e8ce582814c177028c34dc37d98bdf48d883ada1","observation_id":"32b6a08f-3524-4360-8b11-de9e3173d032","resolution":{"observed_at":"2026-08-11T12:40:45.727688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.14002","last_updated":"2024-12-18T16:17:25Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-14T22:23:08.376654Z","submitted_at":"2024-12-18T16:17:25Z","title":"Operator Splitting for Convex Constrained Markov Decision Processes"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":4,"verified_fuzzy":42},"total_outbound_references":62},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2412.14002."}