{"as_of":"2026-08-13T23:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:31da73a1f694f567c3a7793737f08dbc8a41ed426222ece670e2291938bdc26d","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":"2026-08-13T06:32:02.005865+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":"2026-07-11T22:10:49.683444Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2101.09192","last_updated":"2021-01-22T16:27:34Z","snapshot_observed_at":"2026-08-13T06:42:02.078721Z","submitted_at":"2021-01-22T16:27:34Z","title":"Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.09192","snapshot_observed_at":"2026-07-11T22:10:49.683444Z","title":"Gravity optimizer: a kinematic approach on optimization in deep learning.arXiv preprint arXiv:2101.09192, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.04033","last_updated":"2026-07-04T21:27:05Z","snapshot_observed_at":"2026-08-07T05:07:10.107694Z","submitted_at":"2026-07-04T21:27:05Z","title":"OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-11T22:10:49.683444Z"},"links":{"cited_paper":"/paper/2101.09192","citing_paper":"/paper/2607.04033"},"observation_digest":"sha256:43349bfdabfbdd85b07cd6392892fa7f884fb9de2db7f57c1503bd939b615dff","observation_id":"8efa490a-61d5-474e-9e6e-7240d5f0fccf","resolution":{"observed_at":"2026-07-11T22:10:49.683444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2101.09192/citation-record","integrity":"/paper/2101.09192/integrity","json":"/paper/2101.09192/citation-record.json","paper":"/paper/2101.09192"},"outbound":[],"paper":{"arxiv_id":"2101.09192","last_updated":"2021-01-22T16:27:34Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T06:42:02.078721Z","submitted_at":"2021-01-22T16:27:34Z","title":"Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2101.09192."}