{"as_of":"2026-08-08T12:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ca2b331d3d6c89306a95608b4fd66f092a29e46a19c90b1b2feedd9700482cbe","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T10:46:06.685339Z","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":"2026-08-07T10:46:06.730996Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.01439","last_updated":"2024-06-20T12:04:28Z","snapshot_observed_at":"2026-07-06T18:24:30.706727Z","submitted_at":"2024-06-03T15:29:46Z","title":"Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients","version":2},"cited_work":{"arxiv_id":"2406.01439","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.01439","snapshot_observed_at":"2026-08-07T10:46:06.730996Z","title":"Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients","venue":"cs.LG","work_id":"41cfa36a-41c2-4c06-862a-d8dd1e0c0519","year":2024},"citing_paper":{"arxiv_id":"2506.04531","last_updated":"2025-06-05T00:48:55Z","snapshot_observed_at":"2026-08-07T10:37:29.647793Z","submitted_at":"2025-06-05T00:48:55Z","title":"HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T10:46:06.685339Z"},"links":{"cited_paper":"/paper/2406.01439","citing_paper":"/paper/2506.04531"},"observation_digest":"sha256:d516da10efe61eee765296d9b4efa0f2cd3002c9dfe876eea00b7abf4f29728a","observation_id":"549790fa-ed03-41d3-b02c-60da6f815395","resolution":{"observed_at":"2026-08-07T10:46:06.737552Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.01439/citation-record","integrity":"/paper/2406.01439/integrity","json":"/paper/2406.01439/citation-record.json","paper":"/paper/2406.01439"},"outbound":[],"paper":{"arxiv_id":"2406.01439","last_updated":"2024-06-20T12:04:28Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:24:30.706727Z","submitted_at":"2024-06-03T15:29:46Z","title":"Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.01439."}