{"as_of":"2026-08-11T11:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:07ab3724c1c7382465a88d074bb2dc3717be68bbbda9db4381af7ff603e0409a","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":"2026-08-11T06:34:44.6726+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":"2026-08-06T19:50:41.097865Z","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":"2026-08-06T19:48:36.716576Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2204.01390","last_updated":"2023-02-16T12:04:38Z","snapshot_observed_at":"2026-07-06T12:56:21.567189Z","submitted_at":"2022-04-04T11:17:43Z","title":"A Comprehensive Survey on Automated Machine Learning for Recommendations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.01390","snapshot_observed_at":"2026-08-06T19:50:41.097865Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04636","last_updated":"2025-07-07T03:38:09Z","snapshot_observed_at":"2026-08-06T19:40:58.540038Z","submitted_at":"2025-07-07T03:38:09Z","title":"Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:50:41.097865Z"},"links":{"cited_paper":"/paper/2204.01390","citing_paper":"/paper/2507.04636"},"observation_digest":"sha256:6315a06c9435a365250f3a6cd69df50a2ce5983b1910cb0a6342fd2ed93bfb13","observation_id":"d23f2131-e4c5-494c-a719-0fc21a2f165c","resolution":{"observed_at":"2026-08-06T19:50:41.097865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.01390","last_updated":"2023-02-16T12:04:38Z","snapshot_observed_at":"2026-07-06T12:56:21.567189Z","submitted_at":"2022-04-04T11:17:43Z","title":"A Comprehensive Survey on Automated Machine Learning for Recommendations","version":2},"cited_work":{"arxiv_id":"2204.01390","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.01390","snapshot_observed_at":"2026-08-06T19:48:36.716576Z","title":"A Comprehensive Survey on Automated Machine Learning for Recommendations","venue":"cs.IR","work_id":"8a6c1557-8f12-40ef-ab2c-83a3a5145e3c","year":2022},"citing_paper":{"arxiv_id":"2507.04671","last_updated":"2025-08-28T02:55:48Z","snapshot_observed_at":"2026-08-09T15:24:33.647354Z","submitted_at":"2025-07-07T05:22:55Z","title":"DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T19:48:31.733851Z"},"links":{"cited_paper":"/paper/2204.01390","citing_paper":"/paper/2507.04671"},"observation_digest":"sha256:4d3b9d539ad22274adec4c1476492951dad672b40818168ebbe6cd1f4a6059d1","observation_id":"239ece0b-f33a-46e0-a2d7-3a958074d094","resolution":{"observed_at":"2026-08-06T19:48:36.821431Z","resolver_source":"local_arxiv","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/2204.01390/citation-record","integrity":"/paper/2204.01390/integrity","json":"/paper/2204.01390/citation-record.json","paper":"/paper/2204.01390"},"outbound":[],"paper":{"arxiv_id":"2204.01390","last_updated":"2023-02-16T12:04:38Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-07-06T12:56:21.567189Z","submitted_at":"2022-04-04T11:17:43Z","title":"A Comprehensive Survey on Automated Machine Learning for Recommendations"},"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 2 inbound Pith citation observations for arXiv:2204.01390."}