{"as_of":"2026-08-19T17:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f9ebb91476d59f2376d49ce422300630bb725693be422f2625a2d65ef6db29fb","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":"2026-08-19T06:32:44.657259+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":"2026-08-12T11:40:20.009337Z","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":"2026-07-01T19:26:00.781660Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1902.08967","last_updated":"2019-10-09T13:54:45Z","snapshot_observed_at":"2026-08-18T08:56:04.844668Z","submitted_at":"2019-02-24T15:52:09Z","title":"An Online Learning Approach to Model Predictive Control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.08967","snapshot_observed_at":"2026-08-12T11:40:20.009337Z","title":"An on- line learning approach to model predictive control,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2412.00086","last_updated":"2024-11-27T03:33:42Z","snapshot_observed_at":"2026-08-18T13:02:04.803542Z","submitted_at":"2024-11-27T03:33:42Z","title":"Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T11:40:20.009337Z"},"links":{"cited_paper":"/paper/1902.08967","citing_paper":"/paper/2412.00086"},"observation_digest":"sha256:f5760d4028e31d2e01687e92977b1f1c399619af4c3021dfce106ea66895d5c8","observation_id":"18800e33-7c57-4fdb-bde1-5bde94113c5f","resolution":{"observed_at":"2026-08-12T11:40:20.009337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.08967","last_updated":"2019-10-09T13:54:45Z","snapshot_observed_at":"2026-08-18T08:56:04.844668Z","submitted_at":"2019-02-24T15:52:09Z","title":"An Online Learning Approach to Model Predictive Control","version":3},"cited_work":{"arxiv_id":"1902.08967","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.08967","snapshot_observed_at":"2026-07-01T19:26:00.781660Z","title":"An on- line learning approach to model predictive control,","venue":null,"work_id":"858ed6d6-ae22-4289-9073-583e5d38d528","year":1902},"citing_paper":{"arxiv_id":"2605.30778","last_updated":"2026-05-29T03:10:44Z","snapshot_observed_at":"2026-08-18T08:45:25.022811Z","submitted_at":"2026-05-29T03:10:44Z","title":"Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T22:33:38.225735Z"},"links":{"cited_paper":"/paper/1902.08967","citing_paper":"/paper/2605.30778"},"observation_digest":"sha256:dea0464fb1995646f1807c4ea1e2c4061d4267f9994978c7ca70216673c36766","observation_id":"8bbba2d8-85e9-4897-acf4-e74b6d60261d","resolution":{"observed_at":"2026-07-01T19:26:00.783077Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1902.08967/citation-record","integrity":"/paper/1902.08967/integrity","json":"/paper/1902.08967/citation-record.json","paper":"/paper/1902.08967"},"outbound":[],"paper":{"arxiv_id":"1902.08967","last_updated":"2019-10-09T13:54:45Z","latest_version":3,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-18T08:56:04.844668Z","submitted_at":"2019-02-24T15:52:09Z","title":"An Online Learning Approach to Model Predictive Control"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1902.08967."}