{"as_of":"2026-08-08T10:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d859fbe4052ac43d87f00c7822a122a6515c7b2f224babcd6620c575942fcd1","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:51:54.180605Z","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":"2026-07-03T04:07:37.125092Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.01528","last_updated":"2022-04-05T13:45:00Z","snapshot_observed_at":"2026-07-06T11:44:05.282403Z","submitted_at":"2021-09-03T13:52:32Z","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01528","snapshot_observed_at":"2026-08-06T21:51:54.180605Z","title":"Lightautoml: Automl solution for a large financial services ecosystem,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23314","last_updated":"2025-06-29T16:12:41Z","snapshot_observed_at":"2026-08-06T21:43:59.046570Z","submitted_at":"2025-06-29T16:12:41Z","title":"Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T21:51:54.180605Z"},"links":{"cited_paper":"/paper/2109.01528","citing_paper":"/paper/2506.23314"},"observation_digest":"sha256:ceac8b89d71fc58adfb036921d3c74ba7b550a44085982769e375384ed77050b","observation_id":"a1a4a066-7c06-4eb8-9c61-a122fc86d846","resolution":{"observed_at":"2026-08-06T21:51:54.180605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01528","last_updated":"2022-04-05T13:45:00Z","snapshot_observed_at":"2026-07-06T11:44:05.282403Z","submitted_at":"2021-09-03T13:52:32Z","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01528","snapshot_observed_at":"2026-08-06T17:05:04.369380Z","title":"arXiv preprint ArXiv:2109.01528","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11901","last_updated":"2025-07-16T04:34:02Z","snapshot_observed_at":"2026-08-06T16:56:23.483253Z","submitted_at":"2025-07-16T04:34:02Z","title":"Imbalanced Regression Pipeline Recommendation","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-06T17:05:04.369380Z"},"links":{"cited_paper":"/paper/2109.01528","citing_paper":"/paper/2507.11901"},"observation_digest":"sha256:8ad3a33bc11cf57e9f74229bb46b040bb153b5430fee7737b18f188d93250e7e","observation_id":"3d520d60-58a8-4a9c-96f1-cf2e9b4e9853","resolution":{"observed_at":"2026-08-06T17:05:04.369380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01528","last_updated":"2022-04-05T13:45:00Z","snapshot_observed_at":"2026-07-06T11:44:05.282403Z","submitted_at":"2021-09-03T13:52:32Z","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem","version":2},"cited_work":{"arxiv_id":"2109.01528","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.01528","snapshot_observed_at":"2026-07-03T04:07:37.125092Z","title":"Lightautoml: Automl solution for a large financial services ecosystem","venue":null,"work_id":"355c4472-f23d-4118-b2d1-563d926a5411","year":null},"citing_paper":{"arxiv_id":"2508.10177","last_updated":"2026-04-22T18:28:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-08-13T20:29:56Z","title":"KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T22:22:19.478156Z"},"links":{"cited_paper":"/paper/2109.01528","citing_paper":"/paper/2508.10177"},"observation_digest":"sha256:bb660198d96a58ef708294ba39e1adb8dc0320b57c6318311cb940cc1c185b77","observation_id":"14714d9b-6e0a-4d41-af00-73d388b78834","resolution":{"observed_at":"2026-05-18T22:22:51.738847Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01528","last_updated":"2022-04-05T13:45:00Z","snapshot_observed_at":"2026-07-06T11:44:05.282403Z","submitted_at":"2021-09-03T13:52:32Z","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01528","snapshot_observed_at":"2026-08-04T14:52:49.910664Z","title":"Lightautoml: Automl solution for a large financial services ecosystem, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.22768","last_updated":"2026-07-28T18:10:53Z","snapshot_observed_at":"2026-08-07T08:13:28.206502Z","submitted_at":"2025-09-26T17:20:27Z","title":"ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generation","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-04T14:52:49.910664Z"},"links":{"cited_paper":"/paper/2109.01528","citing_paper":"/paper/2509.22768"},"observation_digest":"sha256:c3974ecf6a2bf29c2e852702c6393013df58028c7b242afdd74daa55d1385146","observation_id":"da2f3360-d57d-4ea2-b2bf-ee6767351e45","resolution":{"observed_at":"2026-08-04T14:52:49.910664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01528","last_updated":"2022-04-05T13:45:00Z","snapshot_observed_at":"2026-07-06T11:44:05.282403Z","submitted_at":"2021-09-03T13:52:32Z","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem","version":2},"cited_work":{"arxiv_id":"2109.01528","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.01528","snapshot_observed_at":"2026-07-03T04:07:37.125092Z","title":"Lightautoml: Automl solution for a large financial services ecosystem","venue":null,"work_id":"355c4472-f23d-4118-b2d1-563d926a5411","year":null},"citing_paper":{"arxiv_id":"2606.10725","last_updated":"2026-06-10T06:02:35Z","snapshot_observed_at":"2026-08-06T14:17:15.570656Z","submitted_at":"2026-06-09T11:33:46Z","title":"Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-06-27T14:07:47.862207Z"},"links":{"cited_paper":"/paper/2109.01528","citing_paper":"/paper/2606.10725"},"observation_digest":"sha256:6ab0a102b637298543be27d217c652060ac0c93df8525705e70151fb6e9ab172","observation_id":"1217d351-7cda-468b-9f67-3eb7bf0a9670","resolution":{"observed_at":"2026-07-03T04:07:37.126429Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2109.01528/citation-record","integrity":"/paper/2109.01528/integrity","json":"/paper/2109.01528/citation-record.json","paper":"/paper/2109.01528"},"outbound":[],"paper":{"arxiv_id":"2109.01528","last_updated":"2022-04-05T13:45:00Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:44:05.282403Z","submitted_at":"2021-09-03T13:52:32Z","title":"LightAutoML: AutoML Solution for a Large Financial Services Ecosystem"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2109.01528."}