{"as_of":"2026-08-20T15:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2eac70a090b500891b0849eaf1edf7d000548d66b552f9f18660ac9b9f631339","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":"2026-08-20T06:33:59.587034+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":"2026-08-12T15:38:59.057934Z","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":"2026-07-02T16:57:09.569378Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1704.07807","last_updated":"2019-06-18T02:26:10Z","snapshot_observed_at":"2026-08-17T15:08:28.208539Z","submitted_at":"2017-04-25T17:36:15Z","title":"A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.07807","snapshot_observed_at":"2026-08-12T15:38:59.057934Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.14166","last_updated":"2024-12-17T09:38:53Z","snapshot_observed_at":"2026-08-16T15:53:48.158207Z","submitted_at":"2024-11-21T14:23:06Z","title":"SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T15:38:59.057934Z"},"links":{"cited_paper":"/paper/1704.07807","citing_paper":"/paper/2411.14166"},"observation_digest":"sha256:355b0c22e152c1a5e503e7006b815a03504d6fae349f24f1548c3f0043ea3711","observation_id":"3b9e30e5-36d3-4fd3-a569-8c5d1e76e5f4","resolution":{"observed_at":"2026-08-12T15:38:59.057934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.07807","last_updated":"2019-06-18T02:26:10Z","snapshot_observed_at":"2026-08-17T15:08:28.208539Z","submitted_at":"2017-04-25T17:36:15Z","title":"A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates","version":2},"cited_work":{"arxiv_id":"1704.07807","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1704.07807","snapshot_observed_at":"2026-07-02T16:57:09.569378Z","title":null,"venue":null,"work_id":"9b337522-4047-4b23-adbc-526bb0a3dd6d","year":2019},"citing_paper":{"arxiv_id":"2606.07496","last_updated":"2026-06-05T17:51:11Z","snapshot_observed_at":"2026-08-08T00:02:41.560689Z","submitted_at":"2026-06-05T17:51:11Z","title":"Accelerated Decentralized Stochastic Gradient Descent for Strongly Convex Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T22:18:00.424977Z"},"links":{"cited_paper":"/paper/1704.07807","citing_paper":"/paper/2606.07496"},"observation_digest":"sha256:94e88c7ee42e3341cf70014fffa24dcd5c33cbcfa915a6748d352b3c6fbce23d","observation_id":"5e822fdf-29fb-4be5-9998-3ccbe758ff9d","resolution":{"observed_at":"2026-07-02T16:57:09.570666Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1704.07807/citation-record","integrity":"/paper/1704.07807/integrity","json":"/paper/1704.07807/citation-record.json","paper":"/paper/1704.07807"},"outbound":[],"paper":{"arxiv_id":"1704.07807","last_updated":"2019-06-18T02:26:10Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-08-17T15:08:28.208539Z","submitted_at":"2017-04-25T17:36:15Z","title":"A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1704.07807."}