{"as_of":"2026-08-23T12:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:52fd6b99ca267f7a7d7606e3fb61f04cee011dbf6989bac41c0312216bfaa6e2","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T15:55:16.307904Z","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":"2026-07-04T16:29:57.852260Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","snapshot_observed_at":"2026-08-19T18:09:50.969383Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04439","snapshot_observed_at":"2026-08-10T19:57:02.516726Z","title":"SO(2)-equivariant reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09571","last_updated":"2025-01-16T14:45:12Z","snapshot_observed_at":"2026-08-17T22:53:59.308694Z","submitted_at":"2025-01-16T14:45:12Z","title":"MatrixNet: Learning over symmetry groups using learned group representations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T19:57:02.516726Z"},"links":{"cited_paper":"/paper/2203.04439","citing_paper":"/paper/2501.09571"},"observation_digest":"sha256:7c55a41086d0518bdbb191d795cf1d4aa83eba65445563c577159aef38c5e094","observation_id":"7c46dbd2-e422-44b4-8a71-4c86dbd897d0","resolution":{"observed_at":"2026-08-10T19:57:02.516726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","snapshot_observed_at":"2026-08-19T18:09:50.969383Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04439","snapshot_observed_at":"2026-08-07T15:17:40.347663Z","title":"So(2)-equivariant reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16035","last_updated":"2026-07-29T14:32:31Z","snapshot_observed_at":"2026-08-12T22:54:26.805184Z","submitted_at":"2025-05-21T21:29:18Z","title":"Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T15:17:40.347663Z"},"links":{"cited_paper":"/paper/2203.04439","citing_paper":"/paper/2505.16035"},"observation_digest":"sha256:9e1189549720ec3982c0f168a742d45860f45435369abc4786dd04110e68eb12","observation_id":"626cf6a7-1f35-4074-b331-9caa6d1f430d","resolution":{"observed_at":"2026-08-07T15:17:40.347663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","snapshot_observed_at":"2026-08-19T18:09:50.969383Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04439","snapshot_observed_at":"2026-08-15T15:55:16.307904Z","title":"so(2)-equivariant reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-18T18:09:44.069780Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.307904Z"},"links":{"cited_paper":"/paper/2203.04439","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:86266780c5c76658c9b230e58b851b696a3c929f1c4b75fef1e58bbb6f66d8a6","observation_id":"0c077ab3-371a-4a59-9d12-05325d456fc2","resolution":{"observed_at":"2026-08-15T15:55:16.307904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","snapshot_observed_at":"2026-08-19T18:09:50.969383Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2203.04439","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.04439","snapshot_observed_at":"2026-07-04T16:29:57.852260Z","title":"Jingyun Yang, Zi-ang Cao, Congyue Deng, Rika Antonova, Shuran Song, and Jeannette Bohg","venue":null,"work_id":"5653b5c6-442a-4558-af59-7693e93d2f26","year":2022},"citing_paper":{"arxiv_id":"2606.19784","last_updated":"2026-06-18T04:36:57Z","snapshot_observed_at":"2026-08-01T20:09:52.472703Z","submitted_at":"2026-06-18T04:36:57Z","title":"EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T17:29:08.456346Z"},"links":{"cited_paper":"/paper/2203.04439","citing_paper":"/paper/2606.19784"},"observation_digest":"sha256:df98b26209f93df261d752a7fe6bb7848811ed02b96140f6e6053953219be4b3","observation_id":"061b2673-ff66-4fa4-b6e2-66a35cbd47ce","resolution":{"observed_at":"2026-07-04T03:59:32.848459Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","snapshot_observed_at":"2026-08-19T18:09:50.969383Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2203.04439","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.04439","snapshot_observed_at":"2026-07-04T16:29:57.852260Z","title":"Jingyun Yang, Zi-ang Cao, Congyue Deng, Rika Antonova, Shuran Song, and Jeannette Bohg","venue":null,"work_id":"5653b5c6-442a-4558-af59-7693e93d2f26","year":2022},"citing_paper":{"arxiv_id":"2606.24946","last_updated":"2026-06-23T06:44:27Z","snapshot_observed_at":"2026-08-21T14:17:26.625810Z","submitted_at":"2026-06-23T06:44:27Z","title":"Conformal Orbit-Valid Trust Horizons for Equivariant World Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T00:29:59.636713Z"},"links":{"cited_paper":"/paper/2203.04439","citing_paper":"/paper/2606.24946"},"observation_digest":"sha256:60a85abc273082e18b49eaafe1a0da6374f64907fc4e0070c2709219baa31a4a","observation_id":"3ebfa213-ebe3-4702-a585-394168ec83a5","resolution":{"observed_at":"2026-07-04T16:29:57.854362Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2203.04439/citation-record","integrity":"/paper/2203.04439/integrity","json":"/paper/2203.04439/citation-record.json","paper":"/paper/2203.04439"},"outbound":[],"paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-19T18:09:50.969383Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant 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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2203.04439."}