{"as_of":"2026-08-10T06:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e45b56d9e26ace40d45fcd14142228eef08bf322a9820926b2b8b9a42ec77c88","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T14:49:29.137552Z","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":"2026-05-12T06:11:22.839895Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.02829","last_updated":"2020-02-07T15:01:20Z","snapshot_observed_at":"2026-07-06T08:55:37.851828Z","submitted_at":"2020-02-07T15:01:20Z","title":"Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)","version":1},"cited_work":{"arxiv_id":"2002.02829","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.02829","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Off-policy maximum entropy reinforcement learning: Soft actor-critic with advantage weighted mixture policy (sac-awmp)","venue":null,"work_id":"a8cbae14-0bae-4b06-80fa-a94d54436cda","year":2002},"citing_paper":{"arxiv_id":"2605.09157","last_updated":"2026-05-09T20:37:52Z","snapshot_observed_at":"2026-08-02T18:03:50.564757Z","submitted_at":"2026-05-09T20:37:52Z","title":"Revisiting Mixture Policies in Entropy-Regularized Actor-Critic","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-12T04:33:08.990277Z"},"links":{"cited_paper":"/paper/2002.02829","citing_paper":"/paper/2605.09157"},"observation_digest":"sha256:38a3eaf87bffa556012c5a1c4c77f9ed1da9300d139fdebba7855d6f0f2b66bd","observation_id":"9bdc9302-e4c8-40e7-962e-0a641dce73a8","resolution":{"observed_at":"2026-05-12T06:11:22.842950Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.02829","last_updated":"2020-02-07T15:01:20Z","snapshot_observed_at":"2026-07-06T08:55:37.851828Z","submitted_at":"2020-02-07T15:01:20Z","title":"Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.02829","snapshot_observed_at":"2026-07-14T08:47:01.737491Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10854","last_updated":"2026-07-12T17:36:28Z","snapshot_observed_at":"2026-08-08T10:14:56.039492Z","submitted_at":"2026-07-12T17:36:28Z","title":"Autonomous Transition State Search with Soft Actor-Critic Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T08:47:01.737491Z"},"links":{"cited_paper":"/paper/2002.02829","citing_paper":"/paper/2607.10854"},"observation_digest":"sha256:d65e9774d4c47e0918541b814cba851dccb8449fd395d90e31610e3e53d75950","observation_id":"9bd90051-df4b-4f1c-a682-047eff9bd130","resolution":{"observed_at":"2026-07-14T08:47:01.737491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.02829","last_updated":"2020-02-07T15:01:20Z","snapshot_observed_at":"2026-07-06T08:55:37.851828Z","submitted_at":"2020-02-07T15:01:20Z","title":"Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.02829","snapshot_observed_at":"2026-07-30T14:49:29.131546Z","title":"Zhimin Hou, Kuangen Zhang, Yi Wan, Dongyu Li, Chenglong Fu, and Haoyong Yu","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.23726","last_updated":"2026-07-26T15:41:52Z","snapshot_observed_at":"2026-08-08T06:25:45.831329Z","submitted_at":"2026-07-26T15:41:52Z","title":"Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-30T14:49:29.131546Z"},"links":{"cited_paper":"/paper/2002.02829","citing_paper":"/paper/2607.23726"},"observation_digest":"sha256:63f211a85d71278a281941d96cfb9a3cd3ac4d2123fd5b2a429a96b74a4e788f","observation_id":"e540d67b-2188-418b-b546-5bc8c3045a16","resolution":{"observed_at":"2026-07-30T14:49:29.131546Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.02829","last_updated":"2020-02-07T15:01:20Z","snapshot_observed_at":"2026-07-06T08:55:37.851828Z","submitted_at":"2020-02-07T15:01:20Z","title":"Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.02829","snapshot_observed_at":"2026-07-30T14:49:29.137552Z","title":"Matthias Hutsebaut-Buysse, Kevin Mets, and Steven Latr´ e","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.23726","last_updated":"2026-07-26T15:41:52Z","snapshot_observed_at":"2026-08-08T06:25:45.831329Z","submitted_at":"2026-07-26T15:41:52Z","title":"Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-07-30T14:49:29.137552Z"},"links":{"cited_paper":"/paper/2002.02829","citing_paper":"/paper/2607.23726"},"observation_digest":"sha256:f3de710b470b99ad6fed5f08032df0a9eb7118657872a5ce1fb2eff5167a8b74","observation_id":"1a40db98-bb95-44ea-b329-24d0d2ff932c","resolution":{"observed_at":"2026-07-30T14:49:29.137552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2002.02829/citation-record","integrity":"/paper/2002.02829/integrity","json":"/paper/2002.02829/citation-record.json","paper":"/paper/2002.02829"},"outbound":[],"paper":{"arxiv_id":"2002.02829","last_updated":"2020-02-07T15:01:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:55:37.851828Z","submitted_at":"2020-02-07T15:01:20Z","title":"Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2002.02829."}