{"as_of":"2026-08-18T20:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:99eaf19baf0c6e81b8479b95e547855b3400e7abfd05bb555766d99336976988","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":"2026-08-15T22:25:10.188618Z","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":"2026-05-11T08:11:02.949590Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.17311","last_updated":"2025-02-06T10:59:25Z","snapshot_observed_at":"2026-08-16T12:58:30.147390Z","submitted_at":"2025-01-28T21:48:18Z","title":"RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17311","snapshot_observed_at":"2026-08-15T22:25:10.188618Z","title":"Rlpp: A residual method for zero-shot real-world autonomous racing on scaled platforms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07321","last_updated":"2025-05-12T08:06:36Z","snapshot_observed_at":"2026-08-18T16:15:01.537870Z","submitted_at":"2025-05-12T08:06:36Z","title":"Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T22:25:10.188618Z"},"links":{"cited_paper":"/paper/2501.17311","citing_paper":"/paper/2505.07321"},"observation_digest":"sha256:00430ec610b86df44dd3c0796ee29d92c7c7041e6155b8cf0a3f29a3ebbbe3da","observation_id":"4074b554-4c0b-4be5-bb33-ff40927f5611","resolution":{"observed_at":"2026-08-15T22:25:10.188618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17311","last_updated":"2025-02-06T10:59:25Z","snapshot_observed_at":"2026-08-16T12:58:30.147390Z","submitted_at":"2025-01-28T21:48:18Z","title":"RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17311","snapshot_observed_at":"2026-07-12T23:09:48.353960Z","title":"Rlpp: Reinforcement learning-based path planning for autonomous racing,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.09498","last_updated":"2026-06-21T16:06:34Z","snapshot_observed_at":"2026-08-17T07:52:51.891603Z","submitted_at":"2026-04-10T17:08:44Z","title":"New Scheme Adaption Strategy for Hyperbolic Conservation Laws","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-12T23:09:48.353960Z"},"links":{"cited_paper":"/paper/2501.17311","citing_paper":"/paper/2604.09498"},"observation_digest":"sha256:d8e69c2d4b924d8e8dcd290afb20cb3bf3a010ed7b2b85a745f02a9fa2efc52d","observation_id":"fae892e7-bc50-49a2-bdbd-23bc83a39a4e","resolution":{"observed_at":"2026-07-12T23:09:48.353960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17311","last_updated":"2025-02-06T10:59:25Z","snapshot_observed_at":"2026-08-16T12:58:30.147390Z","submitted_at":"2025-01-28T21:48:18Z","title":"RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms","version":2},"cited_work":{"arxiv_id":"2501.17311","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.17311","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rlpp: Reinforcement learning-based path planning for autonomous racing","venue":null,"work_id":"7c2794bc-231e-440e-bf75-19bfcedf08c0","year":2025},"citing_paper":{"arxiv_id":"2604.09499","last_updated":"2026-04-10T17:12:07Z","snapshot_observed_at":"2026-08-14T08:48:13.470934Z","submitted_at":"2026-04-10T17:12:07Z","title":"Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T16:47:38.002725Z"},"links":{"cited_paper":"/paper/2501.17311","citing_paper":"/paper/2604.09499"},"observation_digest":"sha256:b133f7145ec1d86dd53622a188eb268b17adb99b7dd96c1da46765f77b2ece0b","observation_id":"bb25bf58-23fd-4252-a324-524044a0f6d2","resolution":{"observed_at":"2026-05-11T08:11:02.955149Z","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/2501.17311/citation-record","integrity":"/paper/2501.17311/integrity","json":"/paper/2501.17311/citation-record.json","paper":"/paper/2501.17311"},"outbound":[],"paper":{"arxiv_id":"2501.17311","last_updated":"2025-02-06T10:59:25Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T12:58:30.147390Z","submitted_at":"2025-01-28T21:48:18Z","title":"RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms"},"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:2501.17311."}