{"as_of":"2026-08-05T04:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5063811a206c788cbca4e8775694eb829ef3cd15ef6bae4672ce20b0688d1593","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T05:49:28.713982Z","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":"2026-05-21T05:49:40.600890Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2112.13097","last_updated":"2023-09-24T12:40:18Z","snapshot_observed_at":"2026-07-06T12:22:17.012426Z","submitted_at":"2021-12-24T16:28:18Z","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","version":3},"cited_work":{"arxiv_id":"2112.13097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.13097","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.13097 , year=","venue":null,"work_id":"37375657-c33a-47dc-97c4-e3900647624e","year":null},"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":83,"source":"arxiv_source","source_observed_at":"2026-05-11T02:52:53.588595Z"},"links":{"cited_paper":"/paper/2112.13097","citing_paper":"/paper/2605.07795"},"observation_digest":"sha256:05670a9d48b311e486571949e1e8c693faa09678c377bbe2af73cd23a74664ed","observation_id":"b509828a-75c0-46a8-814a-3eb0f6c21d06","resolution":{"observed_at":"2026-05-11T03:05:54.736859Z","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":"2112.13097","last_updated":"2023-09-24T12:40:18Z","snapshot_observed_at":"2026-07-06T12:22:17.012426Z","submitted_at":"2021-12-24T16:28:18Z","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","version":3},"cited_work":{"arxiv_id":"2112.13097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.13097","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.13097 , year=","venue":null,"work_id":"37375657-c33a-47dc-97c4-e3900647624e","year":null},"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":163,"source":"arxiv_source","source_observed_at":"2026-05-12T01:51:20.003552Z"},"links":{"cited_paper":"/paper/2112.13097","citing_paper":"/paper/2605.08871"},"observation_digest":"sha256:9f8bfcf423f3020bd6caf57b8b10eb2bf25eead57b625d5edd07802c202690e8","observation_id":"263431ec-1c06-4877-a5e8-bdde48713a12","resolution":{"observed_at":"2026-05-12T07:51:32.146953Z","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":"2112.13097","last_updated":"2023-09-24T12:40:18Z","snapshot_observed_at":"2026-07-06T12:22:17.012426Z","submitted_at":"2021-12-24T16:28:18Z","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","version":3},"cited_work":{"arxiv_id":"2112.13097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.13097","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.13097 , year=","venue":null,"work_id":"37375657-c33a-47dc-97c4-e3900647624e","year":null},"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":270,"source":"arxiv_source","source_observed_at":"2026-05-14T19:31:12.149482Z"},"links":{"cited_paper":"/paper/2112.13097","citing_paper":"/paper/2605.13434"},"observation_digest":"sha256:c239613b644c8676b0552a5c1712b3edffa55d6fb667a19e1ec3acb2b03f3dcf","observation_id":"12df0ea9-ade1-4f70-bfab-4dfc55b7fd9a","resolution":{"observed_at":"2026-05-14T19:32:52.055919Z","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":"2112.13097","last_updated":"2023-09-24T12:40:18Z","snapshot_observed_at":"2026-07-06T12:22:17.012426Z","submitted_at":"2021-12-24T16:28:18Z","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","version":3},"cited_work":{"arxiv_id":"2112.13097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.13097","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.13097 , year=","venue":null,"work_id":"37375657-c33a-47dc-97c4-e3900647624e","year":null},"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":165,"source":"arxiv_source","source_observed_at":"2026-05-20T13:08:52.912250Z"},"links":{"cited_paper":"/paper/2112.13097","citing_paper":"/paper/2605.18174"},"observation_digest":"sha256:71e98e7947b398aafb38eae8fd0f0c299834fdcd1b8213196b1e7dfa46782eaa","observation_id":"e3dc0d04-0118-4016-88dd-4abe896bf8a9","resolution":{"observed_at":"2026-05-20T13:13:18.569808Z","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":"2112.13097","last_updated":"2023-09-24T12:40:18Z","snapshot_observed_at":"2026-07-06T12:22:17.012426Z","submitted_at":"2021-12-24T16:28:18Z","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","version":3},"cited_work":{"arxiv_id":"2112.13097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.13097","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.13097 , year=","venue":null,"work_id":"37375657-c33a-47dc-97c4-e3900647624e","year":null},"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":167,"source":"arxiv_source","source_observed_at":"2026-05-21T05:49:28.713982Z"},"links":{"cited_paper":"/paper/2112.13097","citing_paper":"/paper/2605.20866"},"observation_digest":"sha256:1118d6ad8304d549f5d24a283503acc96d41761fd5e594bc420005541bbbf371","observation_id":"efc34ce4-b5b6-427b-8dfa-ecaacd5b4ab6","resolution":{"observed_at":"2026-05-21T05:49:40.602793Z","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/2112.13097/citation-record","integrity":"/paper/2112.13097/integrity","json":"/paper/2112.13097/citation-record.json","paper":"/paper/2112.13097"},"outbound":[],"paper":{"arxiv_id":"2112.13097","last_updated":"2023-09-24T12:40:18Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:22:17.012426Z","submitted_at":"2021-12-24T16:28:18Z","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction"},"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 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2112.13097."}