{"as_of":"2026-08-04T23:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd7987e93a181a127466bb1d4f48c8249e9ba38664403b67dc9fba264c604979","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":"2026-08-04T06:34:03.388597+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":"2026-06-27T03:42:57.288700Z","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":"2026-07-03T17:48:46.109312Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":"2108.04755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.04755","snapshot_observed_at":"2026-07-03T17:48:46.109312Z","title":"arXiv preprint arXiv:2108.04755 , year=","venue":null,"work_id":"95da3572-eb6d-432b-b9d8-b9f5ab768460","year":2021},"citing_paper":{"arxiv_id":"2605.07795","last_updated":"2026-05-08T14:32:41Z","snapshot_observed_at":"2026-07-06T23:20:06.574823Z","submitted_at":"2026-05-08T14:32:41Z","title":"Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-11T02:52:53.588595Z"},"links":{"cited_paper":"/paper/2108.04755","citing_paper":"/paper/2605.07795"},"observation_digest":"sha256:26e94c36ca455e7cf28feb47764045579508a3f1d651def127fdb93bf9d88d9b","observation_id":"ab641495-39a0-4da7-8ff1-7945d2fd7461","resolution":{"observed_at":"2026-05-11T03:05:54.916760Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":"2108.04755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.04755","snapshot_observed_at":"2026-07-03T17:48:46.109312Z","title":"arXiv preprint arXiv:2108.04755 , year=","venue":null,"work_id":"95da3572-eb6d-432b-b9d8-b9f5ab768460","year":2021},"citing_paper":{"arxiv_id":"2605.08871","last_updated":"2026-05-09T10:46:59Z","snapshot_observed_at":"2026-07-31T15:53:03.039634Z","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":164,"source":"arxiv_source","source_observed_at":"2026-05-12T01:51:20.003552Z"},"links":{"cited_paper":"/paper/2108.04755","citing_paper":"/paper/2605.08871"},"observation_digest":"sha256:954795320bb6d5333c16fed4a20e1d72c76c556d13307063610e486d63e9f0aa","observation_id":"013212f3-54b3-448e-881f-a600c10bb67d","resolution":{"observed_at":"2026-05-12T07:51:31.398583Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":"2108.04755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.04755","snapshot_observed_at":"2026-07-03T17:48:46.109312Z","title":"arXiv preprint arXiv:2108.04755 , year=","venue":null,"work_id":"95da3572-eb6d-432b-b9d8-b9f5ab768460","year":2021},"citing_paper":{"arxiv_id":"2605.13434","last_updated":"2026-05-13T12:27:22Z","snapshot_observed_at":"2026-07-06T23:25:01.951546Z","submitted_at":"2026-05-13T12:27:22Z","title":"Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity","version":1},"reference_index":271,"source":"arxiv_source","source_observed_at":"2026-05-14T19:31:12.149482Z"},"links":{"cited_paper":"/paper/2108.04755","citing_paper":"/paper/2605.13434"},"observation_digest":"sha256:c2e66822c672a4ac32c8147406c08220f724558daa8c39352052a17aa7495e26","observation_id":"6a4ef563-07a6-4a58-8d80-7d2c342fcc2d","resolution":{"observed_at":"2026-05-14T19:32:52.081172Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":"2108.04755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.04755","snapshot_observed_at":"2026-07-03T17:48:46.109312Z","title":"arXiv preprint arXiv:2108.04755 , year=","venue":null,"work_id":"95da3572-eb6d-432b-b9d8-b9f5ab768460","year":2021},"citing_paper":{"arxiv_id":"2605.18174","last_updated":"2026-05-18T10:18:02Z","snapshot_observed_at":"2026-07-06T23:29:04.241654Z","submitted_at":"2026-05-18T10:18:02Z","title":"Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method","version":1},"reference_index":166,"source":"arxiv_source","source_observed_at":"2026-05-20T13:08:52.912250Z"},"links":{"cited_paper":"/paper/2108.04755","citing_paper":"/paper/2605.18174"},"observation_digest":"sha256:53f50b17cb47c709276b0596e44e7de6f9cd688e9002ab0651a11be383dd53a7","observation_id":"603e56eb-edc2-4b8a-bfc3-94168a29885f","resolution":{"observed_at":"2026-05-20T13:13:18.449199Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":"2108.04755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.04755","snapshot_observed_at":"2026-07-03T17:48:46.109312Z","title":"arXiv preprint arXiv:2108.04755 , year=","venue":null,"work_id":"95da3572-eb6d-432b-b9d8-b9f5ab768460","year":2021},"citing_paper":{"arxiv_id":"2605.20866","last_updated":"2026-05-20T08:01:45Z","snapshot_observed_at":"2026-08-02T02:54:50.828651Z","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":168,"source":"arxiv_source","source_observed_at":"2026-05-21T05:49:28.713982Z"},"links":{"cited_paper":"/paper/2108.04755","citing_paper":"/paper/2605.20866"},"observation_digest":"sha256:b94ef87d92d9e4e4ed7f38ad72c7b543005f6fe449376752466b6ec057dcda6d","observation_id":"f8b56073-6340-4964-9f17-bec2036d6153","resolution":{"observed_at":"2026-05-21T05:49:40.636147Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","version":1},"cited_work":{"arxiv_id":"2108.04755","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.04755","snapshot_observed_at":"2026-07-03T17:48:46.109312Z","title":"arXiv preprint arXiv:2108.04755 , year=","venue":null,"work_id":"95da3572-eb6d-432b-b9d8-b9f5ab768460","year":2021},"citing_paper":{"arxiv_id":"2606.15832","last_updated":"2026-06-18T09:39:02Z","snapshot_observed_at":"2026-08-03T13:43:00.763355Z","submitted_at":"2026-06-14T14:11:07Z","title":"SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-27T03:42:57.288700Z"},"links":{"cited_paper":"/paper/2108.04755","citing_paper":"/paper/2606.15832"},"observation_digest":"sha256:0d34cc614a7da5d8aa75a164ab5c07b822389566ad5dcfde25612c739b0f66b7","observation_id":"9a6acba9-4df2-465a-ac12-ac228feefdbe","resolution":{"observed_at":"2026-07-03T17:48:46.110880Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2108.04755/citation-record","integrity":"/paper/2108.04755/integrity","json":"/paper/2108.04755/citation-record.json","paper":"/paper/2108.04755"},"outbound":[],"paper":{"arxiv_id":"2108.04755","last_updated":"2021-08-10T15:41:27Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:37:12.299667Z","submitted_at":"2021-08-10T15:41:27Z","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated 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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2108.04755."}