{"as_of":"2026-08-20T16:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b31841442ce5448d682b7f8538176cd92780d159b2536dfd7ad1ab887b31be02","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":"2026-08-20T06:33:59.587034+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":"2026-08-15T20:50:01.744028Z","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":"2026-08-09T13:17:18.803139Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.13038","last_updated":"2024-02-20T14:26:09Z","snapshot_observed_at":"2026-08-16T14:16:56.669482Z","submitted_at":"2024-02-20T14:26:09Z","title":"N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images","version":1},"cited_work":{"arxiv_id":"2402.13038","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.13038","snapshot_observed_at":"2026-08-09T13:17:18.803139Z","title":"N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images","venue":"cs.RO","work_id":"f94c83eb-f5b7-44e2-a337-cb1809b48776","year":2024},"citing_paper":{"arxiv_id":"2502.02133","last_updated":"2025-02-04T09:06:07Z","snapshot_observed_at":"2026-08-13T20:12:45.734139Z","submitted_at":"2025-02-04T09:06:07Z","title":"Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification","version":1},"reference_index":222,"source":"pdf_text","source_observed_at":"2026-08-09T13:17:18.379384Z"},"links":{"cited_paper":"/paper/2402.13038","citing_paper":"/paper/2502.02133"},"observation_digest":"sha256:dd2376845f9d2b1883acb3fb6e1f8932a81be9c27c57d39f400b24005cae2d85","observation_id":"d6ae5d31-f568-4077-b0b7-790b2081aade","resolution":{"observed_at":"2026-08-09T13:17:18.808214Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13038","last_updated":"2024-02-20T14:26:09Z","snapshot_observed_at":"2026-08-16T14:16:56.669482Z","submitted_at":"2024-02-20T14:26:09Z","title":"N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13038","snapshot_observed_at":"2026-08-15T20:50:01.744028Z","title":"N-MPC for deep neural network- based collision avoidance exploiting depth images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11941","last_updated":"2025-05-17T10:08:25Z","snapshot_observed_at":"2026-08-20T05:41:53.133322Z","submitted_at":"2025-05-17T10:08:25Z","title":"Online Synthesis of Control Barrier Functions with Local Occupancy Grid Maps for Safe Navigation in Unknown Environments","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:50:01.744028Z"},"links":{"cited_paper":"/paper/2402.13038","citing_paper":"/paper/2505.11941"},"observation_digest":"sha256:95f02b1eb820e809c28b3cf0e1cc377e7f40989e13a0803f354646e7f9cc54f6","observation_id":"2a1ea52d-d49d-49df-8f57-8f57f3072893","resolution":{"observed_at":"2026-08-15T20:50:01.744028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.13038/citation-record","integrity":"/paper/2402.13038/integrity","json":"/paper/2402.13038/citation-record.json","paper":"/paper/2402.13038"},"outbound":[],"paper":{"arxiv_id":"2402.13038","last_updated":"2024-02-20T14:26:09Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T14:16:56.669482Z","submitted_at":"2024-02-20T14:26:09Z","title":"N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.13038."}