{"as_of":"2026-08-11T21:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cc327eb36c3c838cc710bd359001b0686376edc8c32f1b282ea6dc784562fa3d","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":"2026-08-11T06:34:44.6726+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":"2026-08-06T23:01:04.784230Z","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":"2026-07-02T12:06:55.402839Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.03207","last_updated":"2022-01-31T16:54:53Z","snapshot_observed_at":"2026-08-11T21:13:29.988091Z","submitted_at":"2021-06-06T18:31:08Z","title":"Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.03207","snapshot_observed_at":"2026-08-06T23:01:04.784230Z","title":"Mitigating covariate shift in imitation learning via offline data without great coverage.arXiv preprint arXiv:2106.03207,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.20307","last_updated":"2025-06-25T10:39:32Z","snapshot_observed_at":"2026-08-10T22:23:16.460027Z","submitted_at":"2025-06-25T10:39:32Z","title":"Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:04.784230Z"},"links":{"cited_paper":"/paper/2106.03207","citing_paper":"/paper/2506.20307"},"observation_digest":"sha256:c147296797aae70e3a9becda3406304a89ccab138047cb84649f0dd9ad0f9a8d","observation_id":"d1430f1a-554f-4981-8768-5fe5da62a566","resolution":{"observed_at":"2026-08-06T23:01:04.784230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03207","last_updated":"2022-01-31T16:54:53Z","snapshot_observed_at":"2026-08-11T21:13:29.988091Z","submitted_at":"2021-06-06T18:31:08Z","title":"Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage","version":3},"cited_work":{"arxiv_id":"2106.03207","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.03207","snapshot_observed_at":"2026-07-02T12:06:55.402839Z","title":"arXiv preprint arXiv:2106.03207 , year=","venue":null,"work_id":"2d81aebc-0e14-40d5-adbd-7d39ba67467f","year":null},"citing_paper":{"arxiv_id":"2605.03065","last_updated":"2026-06-26T00:04:01Z","snapshot_observed_at":"2026-07-06T23:16:00.796105Z","submitted_at":"2026-05-04T18:36:40Z","title":"OGPO: Sample Efficient Full-Finetuning of Generative Control Policies","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-08T18:48:56.075160Z"},"links":{"cited_paper":"/paper/2106.03207","citing_paper":"/paper/2605.03065"},"observation_digest":"sha256:739d3728eeb9bf25387cda84685e15633c93a2b7fbae4f02f49bf9a71e00224a","observation_id":"4cac9509-6581-4711-aaf3-e7384073860f","resolution":{"observed_at":"2026-05-09T06:10:42.638461Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03207","last_updated":"2022-01-31T16:54:53Z","snapshot_observed_at":"2026-08-11T21:13:29.988091Z","submitted_at":"2021-06-06T18:31:08Z","title":"Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage","version":3},"cited_work":{"arxiv_id":"2106.03207","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.03207","snapshot_observed_at":"2026-07-02T12:06:55.402839Z","title":"arXiv preprint arXiv:2106.03207 , year=","venue":null,"work_id":"2d81aebc-0e14-40d5-adbd-7d39ba67467f","year":null},"citing_paper":{"arxiv_id":"2606.06418","last_updated":"2026-06-04T17:22:58Z","snapshot_observed_at":"2026-08-02T08:47:15.917924Z","submitted_at":"2026-06-04T17:22:58Z","title":"Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-06-28T02:35:39.845487Z"},"links":{"cited_paper":"/paper/2106.03207","citing_paper":"/paper/2606.06418"},"observation_digest":"sha256:78d056a1a61f6b6c698083f204c09f3601e7b37c884ec7ea064df3fb2c185864","observation_id":"de441bc2-59bf-4aee-a260-48426b8d012b","resolution":{"observed_at":"2026-07-02T12:06:55.404280Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2106.03207/citation-record","integrity":"/paper/2106.03207/integrity","json":"/paper/2106.03207/citation-record.json","paper":"/paper/2106.03207"},"outbound":[],"paper":{"arxiv_id":"2106.03207","last_updated":"2022-01-31T16:54:53Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T21:13:29.988091Z","submitted_at":"2021-06-06T18:31:08Z","title":"Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2106.03207."}