{"as_of":"2026-08-21T04:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fb5a2f5d83f4c7c8968719c85a0a42788ae52ced70261aa5a4075dcbeab412be","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:22:05.909068Z","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":"2026-08-15T23:22:06.477834Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.03683","last_updated":"2024-06-06T01:52:28Z","snapshot_observed_at":"2026-08-17T20:58:49.287954Z","submitted_at":"2024-06-06T01:52:28Z","title":"Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models","version":1},"cited_work":{"arxiv_id":"2406.03683","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.03683","snapshot_observed_at":"2026-08-15T23:22:06.477834Z","title":"Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models","venue":"cs.LG","work_id":"0b604be4-952f-4de3-bc7a-6f043a5533ee","year":2024},"citing_paper":{"arxiv_id":"2505.04992","last_updated":"2025-05-08T06:55:22Z","snapshot_observed_at":"2026-08-18T07:13:46.465859Z","submitted_at":"2025-05-08T06:55:22Z","title":"Boosting Statistic Learning with Synthetic Data from Pretrained Large Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T23:22:05.909068Z"},"links":{"cited_paper":"/paper/2406.03683","citing_paper":"/paper/2505.04992"},"observation_digest":"sha256:5e946ed5dac25ccbec29180ea659232f2f537fe3c46c34faa0c1f0c7101b0585","observation_id":"d97da5a5-84af-47c8-958e-584d4b042147","resolution":{"observed_at":"2026-08-15T23:22:06.481246Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2406.03683/citation-record","integrity":"/paper/2406.03683/integrity","json":"/paper/2406.03683/citation-record.json","paper":"/paper/2406.03683"},"outbound":[],"paper":{"arxiv_id":"2406.03683","last_updated":"2024-06-06T01:52:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T20:58:49.287954Z","submitted_at":"2024-06-06T01:52:28Z","title":"Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion 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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.03683."}