{"as_of":"2026-08-10T23:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aa3d02c3405855d4a822d463e62c8cdb1a3033085937640eb9baeb4bb9550776","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":"2026-08-10T06:31:04.303077+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":"2026-08-08T18:49:52.465284Z","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":"2026-07-04T09:09:43.449770Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.12022","last_updated":"2023-07-16T21:12:43Z","snapshot_observed_at":"2026-07-06T14:55:04.061802Z","submitted_at":"2023-02-08T18:28:48Z","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule","version":3},"cited_work":{"arxiv_id":"2302.12022","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.12022","snapshot_observed_at":"2026-07-04T09:09:43.449770Z","title":null,"venue":null,"work_id":"1bb42dd7-3c35-493f-b301-49f1efe0f7a6","year":2023},"citing_paper":{"arxiv_id":"2412.14291","last_updated":"2026-05-14T16:58:17Z","snapshot_observed_at":"2026-07-06T20:09:33.491422Z","submitted_at":"2024-12-18T19:34:16Z","title":"Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-23T06:23:51.617737Z"},"links":{"cited_paper":"/paper/2302.12022","citing_paper":"/paper/2412.14291"},"observation_digest":"sha256:f0f007075ecfff7e6a415caf496073286cc2c5841a63a02db7d9ddf735145e4e","observation_id":"6fc6404d-b126-4387-9666-8bfb7f9ca6af","resolution":{"observed_at":"2026-05-23T06:25:28.033843Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12022","last_updated":"2023-07-16T21:12:43Z","snapshot_observed_at":"2026-07-06T14:55:04.061802Z","submitted_at":"2023-02-08T18:28:48Z","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12022","snapshot_observed_at":"2026-08-08T18:49:52.465284Z","title":"Dog is sgd’s best friend: A parameter-free dynamic step size schedule, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05600","last_updated":"2025-05-05T05:36:29Z","snapshot_observed_at":"2026-08-09T19:08:23.815423Z","submitted_at":"2025-02-08T15:02:51Z","title":"A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T18:49:52.465284Z"},"links":{"cited_paper":"/paper/2302.12022","citing_paper":"/paper/2502.05600"},"observation_digest":"sha256:1ba11bdd029ac5f936059e051a3c1fc613f49d9970682648a1597eb5e2fc5291","observation_id":"5d9ebbb4-67c5-436b-a6dd-002d3f298dd5","resolution":{"observed_at":"2026-08-08T18:49:52.465284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12022","last_updated":"2023-07-16T21:12:43Z","snapshot_observed_at":"2026-07-06T14:55:04.061802Z","submitted_at":"2023-02-08T18:28:48Z","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12022","snapshot_observed_at":"2026-08-05T11:59:49.585109Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.02046","last_updated":"2025-09-04T19:22:04Z","snapshot_observed_at":"2026-08-10T07:25:52.728334Z","submitted_at":"2025-09-02T07:43:22Z","title":"Fantastic Pretraining Optimizers and Where to Find Them","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-05T11:59:49.585109Z"},"links":{"cited_paper":"/paper/2302.12022","citing_paper":"/paper/2509.02046"},"observation_digest":"sha256:01bf3b79b5ad44d57792bb626b4c1eacdca0506b6bd0b39278d59971f3f270ec","observation_id":"8df1ecf0-b370-44f6-b200-0395f3ee0a19","resolution":{"observed_at":"2026-08-05T11:59:49.585109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12022","last_updated":"2023-07-16T21:12:43Z","snapshot_observed_at":"2026-07-06T14:55:04.061802Z","submitted_at":"2023-02-08T18:28:48Z","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule","version":3},"cited_work":{"arxiv_id":"2302.12022","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.12022","snapshot_observed_at":"2026-07-04T09:09:43.449770Z","title":null,"venue":null,"work_id":"1bb42dd7-3c35-493f-b301-49f1efe0f7a6","year":2023},"citing_paper":{"arxiv_id":"2604.24708","last_updated":"2026-04-27T17:17:28Z","snapshot_observed_at":"2026-07-06T23:10:42.926677Z","submitted_at":"2026-04-27T17:17:28Z","title":"Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T04:08:25.530778Z"},"links":{"cited_paper":"/paper/2302.12022","citing_paper":"/paper/2604.24708"},"observation_digest":"sha256:d4306f442fdb506121fd723ce6fbc9a86104a7dc04a8d0a9460dccbfce185f68","observation_id":"b9aa4aff-fd2e-4e49-aef2-726731ed5d0f","resolution":{"observed_at":"2026-05-11T21:51:19.971064Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12022","last_updated":"2023-07-16T21:12:43Z","snapshot_observed_at":"2026-07-06T14:55:04.061802Z","submitted_at":"2023-02-08T18:28:48Z","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule","version":3},"cited_work":{"arxiv_id":"2302.12022","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.12022","snapshot_observed_at":"2026-07-04T09:09:43.449770Z","title":null,"venue":null,"work_id":"1bb42dd7-3c35-493f-b301-49f1efe0f7a6","year":2023},"citing_paper":{"arxiv_id":"2606.22669","last_updated":"2026-06-21T21:05:59Z","snapshot_observed_at":"2026-08-07T17:50:26.020541Z","submitted_at":"2026-06-21T21:05:59Z","title":"Clipping the Price of Adaptivity at the Tail","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T10:25:20.703566Z"},"links":{"cited_paper":"/paper/2302.12022","citing_paper":"/paper/2606.22669"},"observation_digest":"sha256:d58ee6e74d1816fbcc091fed1bb9ac252a86d78a7bb5c35ee0e04ae55dd61cd4","observation_id":"f6ef02ce-f256-418b-8b4f-273b13a247d2","resolution":{"observed_at":"2026-07-04T09:09:43.451285Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2302.12022/citation-record","integrity":"/paper/2302.12022/integrity","json":"/paper/2302.12022/citation-record.json","paper":"/paper/2302.12022"},"outbound":[],"paper":{"arxiv_id":"2302.12022","last_updated":"2023-07-16T21:12:43Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:55:04.061802Z","submitted_at":"2023-02-08T18:28:48Z","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.12022."}