{"as_of":"2026-08-16T15:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ec3eb7ea5dbd6e350300bfad7c2b1a5d79986ba6f936a96418b880939d4a1649","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:44:56.974655Z","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":"2026-05-21T05:49:40.666173Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11573","snapshot_observed_at":"2026-08-15T20:44:56.974655Z","title":"Khaled, K","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.12409","last_updated":"2025-05-18T13:22:11Z","snapshot_observed_at":"2026-08-15T20:32:03.995719Z","submitted_at":"2025-05-18T13:22:11Z","title":"The Stochastic Multi-Proximal Method for Nonsmooth Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:44:56.974655Z"},"links":{"cited_paper":"/paper/2006.11573","citing_paper":"/paper/2505.12409"},"observation_digest":"sha256:45a3fe868e0f6fbb94f83ab7d07e8f1e5cb3bc062b4f653cc9b446620cc493ec","observation_id":"3697669c-3a0a-432f-930b-8afefdbc7bc6","resolution":{"observed_at":"2026-08-15T20:44:56.974655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11573","snapshot_observed_at":"2026-08-04T21:06:26.258858Z","title":"Khaled, O","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.08233","last_updated":"2025-09-10T02:19:56Z","snapshot_observed_at":"2026-08-14T20:31:36.327522Z","submitted_at":"2025-09-10T02:19:56Z","title":"Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization","version":1},"reference_index":117,"source":"arxiv_source","source_observed_at":"2026-08-04T21:06:26.258858Z"},"links":{"cited_paper":"/paper/2006.11573","citing_paper":"/paper/2509.08233"},"observation_digest":"sha256:138939b0907392a33217d8a0df6bda39b792cec090957f22839ef1977ebd441e","observation_id":"8ef6fcf4-e5c2-46c0-a1f3-c34e70be20eb","resolution":{"observed_at":"2026-08-04T21:06:26.258858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","version":1},"cited_work":{"arxiv_id":"2006.11573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.11573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.11573 , year=","venue":null,"work_id":"7234abf1-864a-456e-bdbc-d00e410d69b7","year":2006},"citing_paper":{"arxiv_id":"2605.07795","last_updated":"2026-05-08T14:32:41Z","snapshot_observed_at":"2026-08-13T15:10:40.244698Z","submitted_at":"2026-05-08T14:32:41Z","title":"Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-05-11T02:52:53.588595Z"},"links":{"cited_paper":"/paper/2006.11573","citing_paper":"/paper/2605.07795"},"observation_digest":"sha256:5e6a5b8ca0afb2aaf4df0da0498501575b72fe04e08086261b3fc0a1e696127f","observation_id":"9bbc596e-1967-4574-979b-174dccca3f5a","resolution":{"observed_at":"2026-05-11T03:05:54.877488Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","version":1},"cited_work":{"arxiv_id":"2006.11573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.11573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.11573 , year=","venue":null,"work_id":"7234abf1-864a-456e-bdbc-d00e410d69b7","year":2006},"citing_paper":{"arxiv_id":"2605.08871","last_updated":"2026-05-09T10:46:59Z","snapshot_observed_at":"2026-08-11T12:27:08.580370Z","submitted_at":"2026-05-09T10:46:59Z","title":"Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction","version":1},"reference_index":178,"source":"arxiv_source","source_observed_at":"2026-05-12T01:51:20.003552Z"},"links":{"cited_paper":"/paper/2006.11573","citing_paper":"/paper/2605.08871"},"observation_digest":"sha256:be5e0f2992740deaa9a859bee9fd029efec73ae50de674caf6239014c75d740b","observation_id":"29a387d1-2f0a-4292-b072-c764c482a0bd","resolution":{"observed_at":"2026-05-12T07:51:33.407134Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","version":1},"cited_work":{"arxiv_id":"2006.11573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.11573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.11573 , year=","venue":null,"work_id":"7234abf1-864a-456e-bdbc-d00e410d69b7","year":2006},"citing_paper":{"arxiv_id":"2605.18174","last_updated":"2026-05-18T10:18:02Z","snapshot_observed_at":"2026-08-15T08:55:48.785673Z","submitted_at":"2026-05-18T10:18:02Z","title":"Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method","version":1},"reference_index":180,"source":"arxiv_source","source_observed_at":"2026-05-20T13:08:52.912250Z"},"links":{"cited_paper":"/paper/2006.11573","citing_paper":"/paper/2605.18174"},"observation_digest":"sha256:6d466c9f7e4bcf2459e3fe986f0555f7696efd97a072a1c6633a7976f2d035f5","observation_id":"3d83436d-b106-4342-99e2-8ac33184266a","resolution":{"observed_at":"2026-05-20T13:13:18.557081Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization","version":1},"cited_work":{"arxiv_id":"2006.11573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.11573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.11573 , year=","venue":null,"work_id":"7234abf1-864a-456e-bdbc-d00e410d69b7","year":2006},"citing_paper":{"arxiv_id":"2605.20866","last_updated":"2026-05-20T08:01:45Z","snapshot_observed_at":"2026-08-13T16:04:20.229116Z","submitted_at":"2026-05-20T08:01:45Z","title":"LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging","version":1},"reference_index":181,"source":"arxiv_source","source_observed_at":"2026-05-21T05:49:28.713982Z"},"links":{"cited_paper":"/paper/2006.11573","citing_paper":"/paper/2605.20866"},"observation_digest":"sha256:f8701cc9d916a5ac8f753b2c36264917880fa18692e3bf1bc83d9d2aecdb70d0","observation_id":"79f00ebc-8426-4860-adab-c99f23c14723","resolution":{"observed_at":"2026-05-21T05:49:40.667550Z","resolver_source":"arxiv_id","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/2006.11573/citation-record","integrity":"/paper/2006.11573/integrity","json":"/paper/2006.11573/citation-record.json","paper":"/paper/2006.11573"},"outbound":[],"paper":{"arxiv_id":"2006.11573","last_updated":"2020-06-20T13:40:27Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T13:11:18.782861Z","submitted_at":"2020-06-20T13:40:27Z","title":"Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization"},"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 6 inbound Pith citation observations for arXiv:2006.11573."}