{"as_of":"2026-08-21T11:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4d450d27549c262f8f4012e5567ac43bb8f82fb5c00c17e5e0414b67b2274887","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:28:18.559511Z","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":"2026-08-11T14:26:11.981082Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.08226","last_updated":"2023-07-17T04:01:48Z","snapshot_observed_at":"2026-08-16T15:15:49.144170Z","submitted_at":"2023-07-17T04:01:48Z","title":"Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?","version":1},"cited_work":{"arxiv_id":"2307.08226","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.08226","snapshot_observed_at":"2026-08-11T14:26:11.981082Z","title":"Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?","venue":"cs.LG","work_id":"7d8b0e40-ef15-4eb5-9b11-a4f397bd2a20","year":2023},"citing_paper":{"arxiv_id":"2412.12024","last_updated":"2024-12-16T17:51:09Z","snapshot_observed_at":"2026-08-13T13:24:31.244742Z","submitted_at":"2024-12-16T17:51:09Z","title":"Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T14:26:11.768143Z"},"links":{"cited_paper":"/paper/2307.08226","citing_paper":"/paper/2412.12024"},"observation_digest":"sha256:6b8e9e3daa9348ea1f5b01cd4f7156b018706080213f24487a4fad1a83da7181","observation_id":"166b5969-b28e-4850-be21-c88d03121327","resolution":{"observed_at":"2026-08-11T14:26:11.992130Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08226","last_updated":"2023-07-17T04:01:48Z","snapshot_observed_at":"2026-08-16T15:15:49.144170Z","submitted_at":"2023-07-17T04:01:48Z","title":"Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08226","snapshot_observed_at":"2026-08-11T14:28:18.559511Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.12237","last_updated":"2024-12-16T17:51:14Z","snapshot_observed_at":"2026-08-15T00:21:21.953549Z","submitted_at":"2024-12-16T17:51:14Z","title":"Equivariant Action Sampling for Reinforcement Learning and Planning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T14:28:18.559511Z"},"links":{"cited_paper":"/paper/2307.08226","citing_paper":"/paper/2412.12237"},"observation_digest":"sha256:84bbd5cb081eb1c12468f41f55a9906c834299f3667ac2e0ad59e1b36578e805","observation_id":"a15ff766-6df8-4554-9eef-695b1b638ce1","resolution":{"observed_at":"2026-08-11T14:28:18.559511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2307.08226/citation-record","integrity":"/paper/2307.08226/integrity","json":"/paper/2307.08226/citation-record.json","paper":"/paper/2307.08226"},"outbound":[],"paper":{"arxiv_id":"2307.08226","last_updated":"2023-07-17T04:01:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T15:15:49.144170Z","submitted_at":"2023-07-17T04:01:48Z","title":"Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.08226."}