{"as_of":"2026-08-18T08:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f88a0727bddf839326f6a94e236946badde85ad9036c27702d4e370e778e0191","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":"2026-08-18T06:34:40.430872+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":"2026-08-11T17:27:56.388099Z","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":"2026-06-30T09:24:32.856696Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.19561","last_updated":"2024-05-14T11:11:43Z","snapshot_observed_at":"2026-08-16T14:47:38.537465Z","submitted_at":"2023-10-30T14:17:32Z","title":"Non-parametric regression for robot learning on manifolds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.19561","snapshot_observed_at":"2026-08-11T17:27:56.388099Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08934","last_updated":"2025-03-12T10:24:31Z","snapshot_observed_at":"2026-08-14T15:05:08.529593Z","submitted_at":"2024-12-12T04:53:39Z","title":"A cheat sheet for probability distributions of orientational data","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T17:27:56.388099Z"},"links":{"cited_paper":"/paper/2310.19561","citing_paper":"/paper/2412.08934"},"observation_digest":"sha256:565968c072551be666ad988c42189ac3f48939686979ce8fe5873390f4f7607f","observation_id":"2810565e-c7f7-4249-9b58-935ca39a981d","resolution":{"observed_at":"2026-08-11T17:27:56.388099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19561","last_updated":"2024-05-14T11:11:43Z","snapshot_observed_at":"2026-08-16T14:47:38.537465Z","submitted_at":"2023-10-30T14:17:32Z","title":"Non-parametric regression for robot learning on manifolds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.19561","snapshot_observed_at":"2026-08-07T04:40:24.459313Z","title":"Non-parametric regression for robot learning on manifolds,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10239","last_updated":"2026-05-30T12:31:54Z","snapshot_observed_at":"2026-08-07T04:29:11.436914Z","submitted_at":"2025-06-11T23:46:57Z","title":"A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T04:40:24.459313Z"},"links":{"cited_paper":"/paper/2310.19561","citing_paper":"/paper/2506.10239"},"observation_digest":"sha256:059b9d17938efeede2c616e3913be36e8bda957a68cdf13d6dad889575460b24","observation_id":"e7d3df1b-cc78-41fd-b44e-489ace182479","resolution":{"observed_at":"2026-08-07T04:40:24.459313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19561","last_updated":"2024-05-14T11:11:43Z","snapshot_observed_at":"2026-08-16T14:47:38.537465Z","submitted_at":"2023-10-30T14:17:32Z","title":"Non-parametric regression for robot learning on manifolds","version":2},"cited_work":{"arxiv_id":"2310.19561","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.19561","snapshot_observed_at":"2026-06-30T09:24:32.856696Z","title":"arXiv preprint arXiv:2310.19561 , year=","venue":null,"work_id":"37ddc12c-4881-43b8-a40a-8d47ed65a696","year":null},"citing_paper":{"arxiv_id":"2606.28738","last_updated":"2026-06-27T05:21:35Z","snapshot_observed_at":"2026-07-07T00:02:47.924620Z","submitted_at":"2026-06-27T05:21:35Z","title":"Composition as Direction: An Active-Set Ray-Based Model for Sparse High-Dimensional Compositional Data","version":1},"reference_index":154,"source":"arxiv_source","source_observed_at":"2026-06-30T09:14:49.892899Z"},"links":{"cited_paper":"/paper/2310.19561","citing_paper":"/paper/2606.28738"},"observation_digest":"sha256:6620b05a85f1c52be480e20416d678691eae77699a2a911d8598ddd9dcbea596","observation_id":"1009e82a-fe0c-4a90-b30b-ba9d41c91911","resolution":{"observed_at":"2026-06-30T09:24:32.858381Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.19561/citation-record","integrity":"/paper/2310.19561/integrity","json":"/paper/2310.19561/citation-record.json","paper":"/paper/2310.19561"},"outbound":[],"paper":{"arxiv_id":"2310.19561","last_updated":"2024-05-14T11:11:43Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T14:47:38.537465Z","submitted_at":"2023-10-30T14:17:32Z","title":"Non-parametric regression for robot learning on manifolds"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.19561."}