{"as_of":"2026-08-14T16:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a38cef5225b07be6abe2c2ca9b5432ed6ab254f2625ffc9cabba220424a7d1e","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T16:57:34.219440Z","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":"2026-08-11T16:57:34.551392Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.15994","last_updated":"2024-11-16T04:21:50Z","snapshot_observed_at":"2026-08-12T23:58:22.763951Z","submitted_at":"2024-05-25T00:35:39Z","title":"Verified Safe Reinforcement Learning for Neural Network Dynamic Models","version":2},"cited_work":{"arxiv_id":"2405.15994","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.15994","snapshot_observed_at":"2026-08-11T16:57:34.551392Z","title":"Verified Safe Reinforcement Learning for Neural Network Dynamic Models","venue":"cs.LG","work_id":"cb1d73dc-f28c-44cb-835d-a11367c7357a","year":2024},"citing_paper":{"arxiv_id":"2412.09584","last_updated":"2025-03-16T06:49:12Z","snapshot_observed_at":"2026-08-13T09:53:44.078504Z","submitted_at":"2024-12-12T18:55:14Z","title":"BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-11T16:57:34.219440Z"},"links":{"cited_paper":"/paper/2405.15994","citing_paper":"/paper/2412.09584"},"observation_digest":"sha256:dfcb3ec72eca0427685be37dd10f703b2b6e62ef0b83ba583150202cb4764967","observation_id":"2b3a95bd-7fa2-419c-82b0-5b05a16ed0b0","resolution":{"observed_at":"2026-08-11T16:57:34.556522Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.15994/citation-record","integrity":"/paper/2405.15994/integrity","json":"/paper/2405.15994/citation-record.json","paper":"/paper/2405.15994"},"outbound":[],"paper":{"arxiv_id":"2405.15994","last_updated":"2024-11-16T04:21:50Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T23:58:22.763951Z","submitted_at":"2024-05-25T00:35:39Z","title":"Verified Safe Reinforcement Learning for Neural Network Dynamic Models"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2405.15994."}