{"as_of":"2026-08-21T05:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:36472c374c240f5c027ae29ccbac524fbe53eab300e30695896ffa2c3ce961f8","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:42:03.341085Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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":"2026-05-15T19:05:15.708770Z","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":"2026-05-15T19:06:30.602990Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2507.15287","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.15287","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2507.15287 (2025)","venue":null,"work_id":"4045ea32-7085-42c1-aa98-b8f741f1b50a","year":2025},"citing_paper":{"arxiv_id":"2603.02259","last_updated":"2026-04-29T14:17:45Z","snapshot_observed_at":"2026-08-12T18:59:48.434438Z","submitted_at":"2026-02-28T00:48:06Z","title":"The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T19:05:15.708770Z"},"links":{"cited_paper":"/paper/2507.15287","citing_paper":"/paper/2603.02259"},"observation_digest":"sha256:9a08923dd580a878608eb5188f99025367773a7293cff0951f78b19d0e914111","observation_id":"b6d982f6-6536-4e7d-8657-a6dbc3977d72","resolution":{"observed_at":"2026-05-15T19:06:30.605792Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.15287/citation-record","integrity":"/paper/2507.15287/integrity","json":"/paper/2507.15287/citation-record.json","paper":"/paper/2507.15287"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.06976","last_updated":"2019-11-19T14:40:14Z","snapshot_observed_at":"2026-08-14T12:28:56.518217Z","submitted_at":"2019-08-19T16:22:20Z","title":"A survey on intrinsic motivation in reinforcement learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.06976","snapshot_observed_at":"2026-08-06T15:42:03.196251Z","title":"A survey on intrinsic motivation in reinforce- ment learning.arXiv preprint arXiv:1908.06976,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.196251Z"},"links":{"cited_paper":"/paper/1908.06976","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:fdb713bf3361f4cc55cc1a11658a7916d76ea2b42ab495801e14201e76087134","observation_id":"771a2466-faf3-4fce-a4ec-1d9a78f44860","resolution":{"observed_at":"2026-08-06T15:42:03.196251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.01387","last_updated":"2019-09-03T18:20:48Z","snapshot_observed_at":"2026-08-20T14:42:08.144375Z","submitted_at":"2019-09-03T18:20:48Z","title":"Making Efficient Use of Demonstrations to Solve Hard Exploration Problems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.01387","snapshot_observed_at":"2026-08-06T15:42:03.259833Z","title":"Making effi- cient use of demonstrations to solve hard exploration problems.arXiv preprint arXiv:1909.01387,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.259833Z"},"links":{"cited_paper":"/paper/1909.01387","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:013d33ab5387f6f64c0b99f6338ad046b5e6dfcf969a0068899a269975767544","observation_id":"f5ca29ce-5881-4ccc-bb21-ecf56bad327d","resolution":{"observed_at":"2026-08-06T15:42:03.259833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.10087","last_updated":"2018-06-26T13:31:37Z","snapshot_observed_at":"2026-08-15T17:49:11.636243Z","submitted_at":"2017-09-28T17:51:13Z","title":"Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.10087","snapshot_observed_at":"2026-08-06T15:42:03.276186Z","title":"Learning complex dexterous manipulation with deep reinforcement learning and demonstrations.arXiv preprint arXiv:1709.10087,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.276186Z"},"links":{"cited_paper":"/paper/1709.10087","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:2156d586c2eec844035cd608b9635f1d7891fbff3ae07acee7d4ac3e95d26218","observation_id":"ea5b3570-07fc-4872-bb6b-313509f92ba0","resolution":{"observed_at":"2026-08-06T15:42:03.276186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12310","last_updated":"2019-06-23T06:11:10Z","snapshot_observed_at":"2026-08-14T16:24:36.036080Z","submitted_at":"2019-05-29T10:18:42Z","title":"Adversarial Imitation Learning from Incomplete Demonstrations","version":3},"cited_work":{"arxiv_id":"1905.12310","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.12310","snapshot_observed_at":"2026-08-06T15:42:03.659626Z","title":"Adversarial Imitation Learning from Incomplete Demonstrations","venue":"cs.LG","work_id":"a149bb15-303e-4f16-91ca-64670c27e1bd","year":2019},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.289555Z"},"links":{"cited_paper":"/paper/1905.12310","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:adfe3f18352835717b370b0d187ff32ddfe4f8a391acce581fdb5cf24ebdf413","observation_id":"d7fdcd91-fafd-4599-9445-694075a5d383","resolution":{"observed_at":"2026-08-06T15:42:03.674069Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09750","last_updated":"2024-05-20T02:12:21Z","snapshot_observed_at":"2026-08-19T19:13:00.081534Z","submitted_at":"2024-01-18T06:32:53Z","title":"Exploration and Anti-Exploration with Distributional Random Network Distillation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09750","snapshot_observed_at":"2026-08-06T15:42:03.316515Z","title":"Exploration and anti-exploration with distributional random network distillation.arXiv preprint arXiv:2401.09750,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.316515Z"},"links":{"cited_paper":"/paper/2401.09750","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:63add649809ccb3f15bd45d328d390cd8c7f54c1a476cf67eade7ccb8fdf9a9b","observation_id":"73993afe-7cd8-40dd-8d48-f2bc7bafdf6a","resolution":{"observed_at":"2026-08-06T15:42:03.316515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19548","last_updated":"2025-04-25T01:53:37Z","snapshot_observed_at":"2026-08-16T13:48:00.199890Z","submitted_at":"2024-05-29T22:23:20Z","title":"RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19548","snapshot_observed_at":"2026-08-06T15:42:03.329662Z","title":"Rlex- plore: Accelerating research in intrinsically-motivated reinforcement learning.arXiv preprint arXiv:2405.19548,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.329662Z"},"links":{"cited_paper":"/paper/2405.19548","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:dd3e85b733f32d0dd2506d12ca38264d2d26b439cfb363e5c4758cd6b472e2d7","observation_id":"abccf5f8-e663-4010-96ac-0ada649605aa","resolution":{"observed_at":"2026-08-06T15:42:03.329662Z","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":"2026-08-06T15:42:03.336321Z","title":"Rui Zhao and V olker Tresp","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.336321Z"},"links":{"citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:75331d3c510425e98a8272cfb17467b7e000679dca9e1118f19dd496c67b7374","observation_id":"566b7923-0178-4262-b848-56a97cad06d8","resolution":{"observed_at":"2026-08-06T15:42:03.336321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05808","last_updated":"2024-03-04T10:09:11Z","snapshot_observed_at":"2026-08-16T14:53:46.625083Z","submitted_at":"2023-10-09T15:45:08Z","title":"An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks","version":3},"cited_work":{"arxiv_id":"2310.05808","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.05808","snapshot_observed_at":"2026-08-06T15:42:03.750010Z","title":"An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks","venue":"cs.RO","work_id":"46096aee-d438-4533-8dca-57b491f52a90","year":2023},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":1989,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.267894Z"},"links":{"cited_paper":"/paper/2310.05808","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:5ba5b1a01e27cfa48dea3b2471a18a06e524280bf2cd2093f503ba84504af477","observation_id":"5fe26d1b-d790-4e1f-9bc7-f28af47c3f64","resolution":{"observed_at":"2026-08-06T15:42:03.757615Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.01954","last_updated":"2018-05-11T21:48:52Z","snapshot_observed_at":"2026-08-14T19:18:37.366856Z","submitted_at":"2018-05-04T22:36:58Z","title":"Behavioral Cloning from Observation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.01954","snapshot_observed_at":"2026-08-06T15:42:03.304388Z","title":"Behavioral cloning from observation.arXiv preprint arXiv:1805.01954, 2018a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.304388Z"},"links":{"cited_paper":"/paper/1805.01954","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:89b7808a69728f14d05d575a022b815149a2fff8ca4e6e5d2f513fc1457976cc","observation_id":"1fddea10-d7da-4f3e-82f9-c2de8a8cb22c","resolution":{"observed_at":"2026-08-06T15:42:03.304388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.12894","last_updated":"2018-10-30T17:44:42Z","snapshot_observed_at":"2026-08-14T18:06:54.993797Z","submitted_at":"2018-10-30T17:44:42Z","title":"Exploration by Random Network Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.12894","snapshot_observed_at":"2026-08-06T15:42:03.203959Z","title":"Exploration by random network distillation.arXiv preprint arXiv:1810.12894,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.203959Z"},"links":{"cited_paper":"/paper/1810.12894","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:10fb005a23a6dd28b6316f1dac15d80f75e08782f10fb3ca3ca88b4b9a4ae7d3","observation_id":"2d9b7790-6cdd-4a25-9deb-11ae6a7a5595","resolution":{"observed_at":"2026-08-06T15:42:03.203959Z","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":"2026-08-06T15:42:04.021765Z","title":"Explorative imitation learning: A path signature approach for continuous environments","venue":null,"work_id":"703a8d08-a32c-406d-8b44-b2ce1f7c978e","year":2024},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.232002Z"},"links":{"citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:ec83b2a36ce14663b0e499c282f793425effa889b4cac028d56577bf33fc994a","observation_id":"3b87e366-a953-463e-9fa6-0c5e4cd585ef","resolution":{"observed_at":"2026-08-06T15:42:04.028789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T15:42:04.050939Z","title":"Sparsedice: Imitation learning for temporally sparse data via regularization","venue":null,"work_id":"d4369488-b87b-484f-8248-6e7ea0e4304e","year":2021},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.210318Z"},"links":{"citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:a9bb5274316a9d2daa1b2bbd4e54953237b9f70f81c42f2ec798c402af4dce0e","observation_id":"86238447-d171-418a-a854-4743cae0ab33","resolution":{"observed_at":"2026-08-06T15:42:04.059332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10947","last_updated":"2019-11-21T22:18:16Z","snapshot_observed_at":"2026-08-05T18:20:08.979986Z","submitted_at":"2019-11-21T22:18:16Z","title":"State Alignment-based Imitation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10947","snapshot_observed_at":"2026-08-06T15:42:03.251702Z","title":"State alignment-based imitation learning","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.251702Z"},"links":{"cited_paper":"/paper/1911.10947","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:858c010de5476a1b51d57990d965f6721b75dcda22f1634a30685f326a3815bb","observation_id":"1f2cc44f-ff3d-4de6-97d8-345a2f33353b","resolution":{"observed_at":"2026-08-06T15:42:03.251702Z","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":"2026-08-06T15:42:03.984013Z","title":"F Grid world We present qualitative results in a gridworld with random walls, where the agent can move in any direction","venue":null,"work_id":"8cb96f1b-ff6d-4727-b6fa-186f3802430c","year":1953},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.341085Z"},"links":{"citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:41628d705e4be954a81621b2bd8ea5aa8ca80dcb6e4da0b8833c6876868fbdd2","observation_id":"fecf03e2-c4df-43ab-baf7-4684449c3f14","resolution":{"observed_at":"2026-08-06T15:42:03.993208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.11248","last_updated":"2018-08-13T18:33:24Z","snapshot_observed_at":"2026-08-14T20:18:41.596978Z","submitted_at":"2017-10-30T21:22:28Z","title":"Learning Robust Rewards with Adversarial Inverse Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.11248","snapshot_observed_at":"2026-08-06T15:42:03.223862Z","title":"Learning robust rewards with adversarial inverse rein- forcement learning.arXiv preprint arXiv:1710.11248,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.223862Z"},"links":{"cited_paper":"/paper/1710.11248","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:5484cebae7d51a52f38efb4ccb627c83fbb3e6d059181ba01baf2385242bdeca","observation_id":"d94434a7-9170-43dc-af91-c00316facab5","resolution":{"observed_at":"2026-08-06T15:42:03.223862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-17T07:51:41.384508Z","submitted_at":"2018-12-13T04:44:29Z","title":"Soft Actor-Critic Algorithms and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05905","snapshot_observed_at":"2026-08-06T15:42:03.243618Z","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T15:42:03.243618Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2507.15287"},"observation_digest":"sha256:f5c9cb4b39caf5413d833b53b3680988874db53c8e57ecf2cdd63c4daed489f0","observation_id":"f348d27f-a54a-432c-b92b-dea35f4c95da","resolution":{"observed_at":"2026-08-06T15:42:03.243618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.15287","last_updated":"2025-07-21T06:38:46Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T20:50:35.708825Z","submitted_at":"2025-07-21T06:38:46Z","title":"Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":16},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2507.15287."}