{"as_of":"2026-08-22T10:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f8bfa3743e4836317f1796c32ff3a3b511453b31ec9ddf6e5f2a0fcff7b1b51f","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T04:59:29.778982Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-17T12:04:10.871465Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1804.07193","last_updated":"2018-07-27T12:40:44Z","snapshot_observed_at":"2026-08-14T19:24:14.319100Z","submitted_at":"2018-04-19T14:29:41Z","title":"Lipschitz Continuity in Model-based Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1804.07193","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.07193","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lipschitz Continuity in Model-based Reinforcement Learning","venue":"cs.LG","work_id":"ba0670fa-c586-4d1b-ac7a-fd1b6233029b","year":2018},"citing_paper":{"arxiv_id":"2409.12917","last_updated":"2024-10-04T17:28:45Z","snapshot_observed_at":"2026-08-16T03:33:39.637646Z","submitted_at":"2024-09-19T17:16:21Z","title":"Training Language Models to Self-Correct via Reinforcement Learning","version":2},"reference_index":295,"source":"arxiv_source","source_observed_at":"2026-05-17T12:04:10.210508Z"},"links":{"cited_paper":"/paper/1804.07193","citing_paper":"/paper/2409.12917"},"observation_digest":"sha256:fd86c5e7f33634786ed631f47c61c252e11b9cd93f4c286b2ce9ca8317bbf263","observation_id":"b542f905-588d-4e63-a3f4-a60e5c47e78c","resolution":{"observed_at":"2026-05-17T12:04:10.873417Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.07193","last_updated":"2018-07-27T12:40:44Z","snapshot_observed_at":"2026-08-14T19:24:14.319100Z","submitted_at":"2018-04-19T14:29:41Z","title":"Lipschitz Continuity in Model-based Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.07193","snapshot_observed_at":"2026-08-07T15:36:21.402923Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14564","last_updated":"2025-05-20T16:24:42Z","snapshot_observed_at":"2026-08-20T12:58:26.873917Z","submitted_at":"2025-05-20T16:24:42Z","title":"Bellman operator convergence enhancements in reinforcement learning algorithms","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:21.402923Z"},"links":{"cited_paper":"/paper/1804.07193","citing_paper":"/paper/2505.14564"},"observation_digest":"sha256:247a60713a8da016752337ea2845928426fb8f94da02f520b4784923e52e7bd0","observation_id":"22af8a4b-41b4-401c-b211-b638843799b6","resolution":{"observed_at":"2026-08-07T15:36:21.402923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.07193","last_updated":"2018-07-27T12:40:44Z","snapshot_observed_at":"2026-08-14T19:24:14.319100Z","submitted_at":"2018-04-19T14:29:41Z","title":"Lipschitz Continuity in Model-based Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.07193","snapshot_observed_at":"2026-08-10T04:59:29.778982Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.07420","last_updated":"2026-08-07T17:05:44Z","snapshot_observed_at":"2026-08-19T12:57:56.677654Z","submitted_at":"2026-08-07T17:05:44Z","title":"Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T04:59:29.778982Z"},"links":{"cited_paper":"/paper/1804.07193","citing_paper":"/paper/2608.07420"},"observation_digest":"sha256:01ad70c20ae8d6f7cab4ccea7688ac7319286656d8313fd1e878ece9778a3b90","observation_id":"bc17e08a-d9e3-4061-b923-471599a71ac0","resolution":{"observed_at":"2026-08-10T04:59:29.778982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1804.07193/citation-record","integrity":"/paper/1804.07193/integrity","json":"/paper/1804.07193/citation-record.json","paper":"/paper/1804.07193"},"outbound":[],"paper":{"arxiv_id":"1804.07193","last_updated":"2018-07-27T12:40:44Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T19:24:14.319100Z","submitted_at":"2018-04-19T14:29:41Z","title":"Lipschitz Continuity in Model-based Reinforcement Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1804.07193."}