{"as_of":"2026-08-17T03:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03c81e926c5418a3e16ca97bc8f4174b294def287f11283407db9acb4c6b9f24","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":"2026-08-16T06:30:59.297886+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":"2026-08-16T10:56:55.303280Z","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":"2026-08-16T13:49:34.757488Z","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":"sha256:0b840d7e397ff525c2dacf40d998b32f4fcf20d56cdb3e9df6a91a5b4aeee29f","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 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15994","last_updated":"2024-11-16T04:21:50Z","snapshot_observed_at":"2026-08-16T13:49:34.757488Z","submitted_at":"2024-05-25T00:35:39Z","title":"Verified Safe Reinforcement Learning for Neural Network Dynamic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15994","snapshot_observed_at":"2026-08-16T10:56:55.303280Z","title":"Verified safe reinforce- ment learning for neural network dynamic models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16879","last_updated":"2025-04-23T16:54:35Z","snapshot_observed_at":"2026-08-16T10:51:20.618622Z","submitted_at":"2025-04-23T16:54:35Z","title":"Learning Verifiable Control Policies Using Relaxed Verification","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T10:56:55.303280Z"},"links":{"cited_paper":"/paper/2405.15994","citing_paper":"/paper/2504.16879"},"observation_digest":"sha256:d82558d9a1f594deeeafb3c85b71b8bb3e8a12a4f1b482c16ec2bb4207ea4523","observation_id":"84f0bdaf-976c-4a8c-82f8-619f873a5b3f","resolution":{"observed_at":"2026-08-16T10:56:55.303280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"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":"2026-08-16T13:49:34.757488Z","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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.15994."}