{"as_of":"2026-08-22T02:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7a0c2ad03d1c756619815d006af91b58640454cf4f6e447aba6582e90679c8f9","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":"2026-08-21T06:32:19.484+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":"2026-06-28T22:15:22.557983Z","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":"2026-07-01T19:36:09.088713Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1710.02410","last_updated":"2018-03-02T16:43:34Z","snapshot_observed_at":"2026-08-16T09:40:06.544671Z","submitted_at":"2017-10-06T14:00:31Z","title":"End-to-end Driving via Conditional Imitation Learning","version":2},"cited_work":{"arxiv_id":"1710.02410","doi":null,"metadata_source":"pith","pith_arxiv_id":"1710.02410","snapshot_observed_at":"2026-07-01T19:36:09.088713Z","title":"End-to-end driving via conditional imitation learn- ing","venue":"cs.RO","work_id":"f346ab57-db26-41bc-b798-5d22d586bd95","year":2017},"citing_paper":{"arxiv_id":"2605.04355","last_updated":"2026-05-05T23:24:45Z","snapshot_observed_at":"2026-08-15T08:25:14.346430Z","submitted_at":"2026-05-05T23:24:45Z","title":"InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-08T17:01:31.011188Z"},"links":{"cited_paper":"/paper/1710.02410","citing_paper":"/paper/2605.04355"},"observation_digest":"sha256:7887a7b8e7eaaa4159987f2e83dfba581281ab3c06ef618779e8c5ebcbf96946","observation_id":"61808404-ac7b-47ee-ab8e-f42a09db407a","resolution":{"observed_at":"2026-05-11T17:51:09.012241Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.02410","last_updated":"2018-03-02T16:43:34Z","snapshot_observed_at":"2026-08-16T09:40:06.544671Z","submitted_at":"2017-10-06T14:00:31Z","title":"End-to-end Driving via Conditional Imitation Learning","version":2},"cited_work":{"arxiv_id":"1710.02410","doi":null,"metadata_source":"pith","pith_arxiv_id":"1710.02410","snapshot_observed_at":"2026-07-01T19:36:09.088713Z","title":"End-to-end driving via conditional imitation learn- ing","venue":"cs.RO","work_id":"f346ab57-db26-41bc-b798-5d22d586bd95","year":2017},"citing_paper":{"arxiv_id":"2605.08975","last_updated":"2026-05-09T14:34:00Z","snapshot_observed_at":"2026-08-16T13:57:14.535175Z","submitted_at":"2026-05-09T14:34:00Z","title":"Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-12T01:52:02.233177Z"},"links":{"cited_paper":"/paper/1710.02410","citing_paper":"/paper/2605.08975"},"observation_digest":"sha256:a3b419a809bfe4e7fb43340c51a94133b107d920c6e58be79a1a2986a9d65c0b","observation_id":"3bee857e-2d99-4968-9a45-8f886dcbaa00","resolution":{"observed_at":"2026-05-12T07:46:50.664151Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.02410","last_updated":"2018-03-02T16:43:34Z","snapshot_observed_at":"2026-08-16T09:40:06.544671Z","submitted_at":"2017-10-06T14:00:31Z","title":"End-to-end Driving via Conditional Imitation Learning","version":2},"cited_work":{"arxiv_id":"1710.02410","doi":null,"metadata_source":"pith","pith_arxiv_id":"1710.02410","snapshot_observed_at":"2026-07-01T19:36:09.088713Z","title":"End-to-end driving via conditional imitation learn- ing","venue":"cs.RO","work_id":"f346ab57-db26-41bc-b798-5d22d586bd95","year":2017},"citing_paper":{"arxiv_id":"2606.00191","last_updated":"2026-05-29T16:11:23Z","snapshot_observed_at":"2026-08-17T23:03:50.060144Z","submitted_at":"2026-05-29T16:11:23Z","title":"Safe2Drive: Evaluating Safe Driving Behaviors of E2E Autonomous Driving Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-28T22:15:22.557983Z"},"links":{"cited_paper":"/paper/1710.02410","citing_paper":"/paper/2606.00191"},"observation_digest":"sha256:967f0421def903a9c2a9da203691fe5ff6c4d5fa0f5ee24339ce70a01fc711cd","observation_id":"7d64b2b5-e3d9-4cde-9a07-08f1f36734dc","resolution":{"observed_at":"2026-07-01T19:36:09.090015Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1710.02410/citation-record","integrity":"/paper/1710.02410/integrity","json":"/paper/1710.02410/citation-record.json","paper":"/paper/1710.02410"},"outbound":[],"paper":{"arxiv_id":"1710.02410","last_updated":"2018-03-02T16:43:34Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T09:40:06.544671Z","submitted_at":"2017-10-06T14:00:31Z","title":"End-to-end Driving via Conditional Imitation Learning"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1710.02410."}