{"as_of":"2026-08-11T05:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9ced2fb0bcdd4f39cd51b189d44eb5c4f9be38bcb64eb99d00d294e5aec16393","coverage":[{"denominator":6,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T18:42:51.246886Z","state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2603.08287/citation-record","integrity":"/paper/2603.08287/integrity","json":"/paper/2603.08287/citation-record.json","paper":"/paper/2603.08287"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:42:51.236520Z","title":"Thus we have R1 = max(2σ p ds, p 16σ2 log(T))≤168α −1/2p max(C, σ2)(ds +d a) log(10(T+R a) max(1, L/C))","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T18:42:51.236520Z"},"links":{"citing_paper":"/paper/2603.08287"},"observation_digest":"sha256:94482a5a1eae61f9ecf5a6e5a5c90aef204f2b93599954bd4fb1c909b0eecc51","observation_id":"4cae30dd-cab3-4bf6-bc34-c6737e1c1f8f","resolution":{"observed_at":"2026-08-02T18:42:51.236520Z","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":"2026-08-02T18:42:51.226121Z","title":"Tail Bounds for Suprema of Gaussian Processes We prove the results given in Section 4.2","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T18:42:51.226121Z"},"links":{"citing_paper":"/paper/2603.08287"},"observation_digest":"sha256:50f144afe2312b89b6e7b6ac383a5926c8905420d19cdebdc262a48f1b7208aa","observation_id":"5f75e07d-dc61-4c25-9d21-0ea2000d86c8","resolution":{"observed_at":"2026-08-02T18:42:51.226121Z","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":"2026-08-02T18:42:51.230986Z","title":"lim n→∞ sup x∈Z (n) ∥f(x)∥ 2 # = lim n→∞ E","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T18:42:51.230986Z"},"links":{"citing_paper":"/paper/2603.08287"},"observation_digest":"sha256:9a6861e5ec6bae8e0e44a0b14704d17d01038d5e34b652d62126f7d0ee4ec2d3","observation_id":"3ede3f01-341e-4ada-8429-47e543160aaf","resolution":{"observed_at":"2026-08-02T18:42:51.230986Z","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":"2026-08-02T18:42:51.241426Z","title":"I{A} NX n=1 V Mn πn,1 (sn,1)−V M⋆ πn,1(sn,1) # ≤E","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T18:42:51.241426Z"},"links":{"citing_paper":"/paper/2603.08287"},"observation_digest":"sha256:1b1bbd2bdc0d67a36ac984eb7b6a30d04daccfb60f0b85176629d9ef098d2d22","observation_id":"c9a8fb10-80b5-4d1a-b1b0-a30f9ca58f05","resolution":{"observed_at":"2026-08-02T18:42:51.241426Z","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":"2026-08-02T18:42:51.246886Z","title":"Using Lemma E.2, one can prove a version of the elliptical potential lemma that accounts for the fact that f (n) is only re-sampled at the end of each episode","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T18:42:51.246886Z"},"links":{"citing_paper":"/paper/2603.08287"},"observation_digest":"sha256:00985ad004f18d845860f03d81b1da49a3f61682d8b97f10c69c9ddc8971bf04","observation_id":"0186a3d0-5402-4e9c-8fe4-6af4e92b26d1","resolution":{"observed_at":"2026-08-02T18:42:51.246886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.02377","last_updated":"2021-07-06T04:01:22Z","snapshot_observed_at":"2026-08-03T13:23:46.649259Z","submitted_at":"2021-07-06T04:01:22Z","title":"A Short Note on the Relationship of Information Gain and Eluder Dimension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.02377","snapshot_observed_at":"2026-08-02T18:42:51.219935Z","title":"M., Lee, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces","version":3},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-02T18:42:51.219935Z"},"links":{"cited_paper":"/paper/2107.02377","citing_paper":"/paper/2603.08287"},"observation_digest":"sha256:f15b2fca64b8a1cb5a7eb5ebc3a7ac90ae9eb9c3898474028fb6f6fbaa721f48","observation_id":"35365608-6bcb-4616-a0d1-236bf7c147ca","resolution":{"observed_at":"2026-08-02T18:42:51.219935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.08287","last_updated":"2026-07-22T17:05:06Z","latest_version":3,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-10T19:51:32.799744Z","submitted_at":"2026-03-09T12:03:25Z","title":"Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces"},"reference_resolution":{"displayed":6,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":6},"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 11 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2603.08287."}