{"as_of":"2026-08-16T09:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fcbfbcd49b4cb767eeddf63b68d863d996a15f030d8c4d8ca14ec3b6ebd871c2","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T15:14:13.026570Z","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":"2026-08-15T15:14:13.198194Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.09058","last_updated":"2024-12-07T20:43:44Z","snapshot_observed_at":"2026-08-13T05:24:41.720347Z","submitted_at":"2023-11-15T15:50:34Z","title":"Improving Deep Learning Optimization through Constrained Parameter Regularization","version":4},"cited_work":{"arxiv_id":"2311.09058","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.09058","snapshot_observed_at":"2026-08-15T15:14:13.198194Z","title":"Improving Deep Learning Optimization through Constrained Parameter Regularization","venue":"cs.LG","work_id":"43d6e773-ba67-44a8-8450-b2935425cc2a","year":2023},"citing_paper":{"arxiv_id":"2608.01284","last_updated":"2026-08-02T14:47:39Z","snapshot_observed_at":"2026-08-15T15:06:50.354371Z","submitted_at":"2026-08-02T14:47:39Z","title":"Training nGPT","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T15:14:13.026570Z"},"links":{"cited_paper":"/paper/2311.09058","citing_paper":"/paper/2608.01284"},"observation_digest":"sha256:3bee807f3f3b13797229d7656738b964f3cd3d3df5294abf89995e3d16c70b01","observation_id":"c25203a5-6042-4b2a-a8d0-1d407d5af8a3","resolution":{"observed_at":"2026-08-15T15:14:13.203577Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2311.09058/citation-record","integrity":"/paper/2311.09058/integrity","json":"/paper/2311.09058/citation-record.json","paper":"/paper/2311.09058"},"outbound":[],"paper":{"arxiv_id":"2311.09058","last_updated":"2024-12-07T20:43:44Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T05:24:41.720347Z","submitted_at":"2023-11-15T15:50:34Z","title":"Improving Deep Learning Optimization through Constrained Parameter Regularization"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2311.09058."}