{"as_of":"2026-08-08T06:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de4340765b6b0eb78eaf989293e08621514c00092d3aa7822ff68f1b25e0705e","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":"2026-05-10T02:35:40.593397Z","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":"2026-05-11T12:56:06.238926Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.15865","last_updated":"2022-10-28T03:20:10Z","snapshot_observed_at":"2026-08-04T12:55:10.405226Z","submitted_at":"2022-10-28T03:20:10Z","title":"Completely Heterogeneous Federated Learning","version":1},"cited_work":{"arxiv_id":"2210.15865","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.15865","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2210.15865 , year=","venue":null,"work_id":"118f551a-3124-4005-828e-04b2e45de487","year":null},"citing_paper":{"arxiv_id":"2604.19015","last_updated":"2026-04-21T03:06:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-21T03:06:24Z","title":"FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-10T02:35:40.593397Z"},"links":{"cited_paper":"/paper/2210.15865","citing_paper":"/paper/2604.19015"},"observation_digest":"sha256:1f155ed44f9761c1daefdc8174855973e92c3c6a2c31eb8e1bef85b40c298942","observation_id":"f5660140-546a-4884-bcd0-036d231036d9","resolution":{"observed_at":"2026-05-11T12:56:06.249239Z","resolver_source":"arxiv_id","status":"verified_exact"},"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/2210.15865/citation-record","integrity":"/paper/2210.15865/integrity","json":"/paper/2210.15865/citation-record.json","paper":"/paper/2210.15865"},"outbound":[],"paper":{"arxiv_id":"2210.15865","last_updated":"2022-10-28T03:20:10Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T12:55:10.405226Z","submitted_at":"2022-10-28T03:20:10Z","title":"Completely Heterogeneous 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":"2026-08-08T06:32:00.761636+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:2210.15865."}