{"as_of":"2026-08-09T17:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:14e3b7b0f3ebe25c9f9e5c71853975931ddc2ca7e5fd94845507e4960f4381f3","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T01:03:43.321236Z","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":"2026-08-07T01:03:43.660949Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2208.11231","last_updated":"2022-12-04T16:37:16Z","snapshot_observed_at":"2026-08-07T21:08:50.876893Z","submitted_at":"2022-08-23T23:33:38Z","title":"Exact Penalty Method for Federated Learning","version":2},"cited_work":{"arxiv_id":"2208.11231","doi":null,"metadata_source":"pith","pith_arxiv_id":"2208.11231","snapshot_observed_at":"2026-08-07T01:03:43.660949Z","title":"Exact Penalty Method for Federated Learning","venue":"cs.LG","work_id":"57edc691-c5c0-45b2-a018-9ab2d2ba20db","year":2022},"citing_paper":{"arxiv_id":"2506.12213","last_updated":"2025-06-13T20:31:17Z","snapshot_observed_at":"2026-08-07T16:12:28.716043Z","submitted_at":"2025-06-13T20:31:17Z","title":"Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T01:03:43.321236Z"},"links":{"cited_paper":"/paper/2208.11231","citing_paper":"/paper/2506.12213"},"observation_digest":"sha256:e9ff60e0aee6837e2cbb0338bde14eac0e236a02e0fade0e600fdd79cd404b9d","observation_id":"04a379ed-b54d-492a-98b5-36f02eaa89f0","resolution":{"observed_at":"2026-08-07T01:03:43.716682Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2208.11231/citation-record","integrity":"/paper/2208.11231/integrity","json":"/paper/2208.11231/citation-record.json","paper":"/paper/2208.11231"},"outbound":[],"paper":{"arxiv_id":"2208.11231","last_updated":"2022-12-04T16:37:16Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T21:08:50.876893Z","submitted_at":"2022-08-23T23:33:38Z","title":"Exact Penalty Method for 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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2208.11231."}