{"as_of":"2026-08-09T12:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:51233f3b63d668d4792836c63d7c21b55322a233090135334bbafb8f39a795f9","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:01:26.489970Z","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":"2026-06-30T16:44:56.431775Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2003.12880","last_updated":"2020-03-28T19:55:24Z","snapshot_observed_at":"2026-08-04T06:02:51.578196Z","submitted_at":"2020-03-28T19:55:24Z","title":"Federated Residual Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.12880","snapshot_observed_at":"2026-08-07T12:01:26.489970Z","title":"Federated residual learning","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.00932","last_updated":"2025-06-01T09:53:54Z","snapshot_observed_at":"2026-08-07T11:52:15.996106Z","submitted_at":"2025-06-01T09:53:54Z","title":"Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:01:26.489970Z"},"links":{"cited_paper":"/paper/2003.12880","citing_paper":"/paper/2506.00932"},"observation_digest":"sha256:0a5c255d1e79cda6f019dcb2df31f175543ff2ffafaf8ba62a543f296560569c","observation_id":"bd18ff49-d22e-42cc-90f5-eb9b14fc5a89","resolution":{"observed_at":"2026-08-07T12:01:26.489970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.12880","last_updated":"2020-03-28T19:55:24Z","snapshot_observed_at":"2026-08-04T06:02:51.578196Z","submitted_at":"2020-03-28T19:55:24Z","title":"Federated Residual Learning","version":1},"cited_work":{"arxiv_id":"2003.12880","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.12880","snapshot_observed_at":"2026-06-30T16:44:56.431775Z","title":"arXiv preprint arXiv:2003.12880 , year=","venue":null,"work_id":"296ced59-5189-415d-bff4-792a287ebb92","year":2003},"citing_paper":{"arxiv_id":"2606.30615","last_updated":"2026-06-29T17:49:33Z","snapshot_observed_at":"2026-07-07T00:04:25.385438Z","submitted_at":"2026-06-29T17:49:33Z","title":"Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage","version":1},"reference_index":124,"source":"arxiv_source","source_observed_at":"2026-06-30T04:41:41.370083Z"},"links":{"cited_paper":"/paper/2003.12880","citing_paper":"/paper/2606.30615"},"observation_digest":"sha256:8e8fe6937e9f129d510fcc2e7c1186917d886961d6dc351adde47e2bb195b820","observation_id":"354ae161-39aa-4b68-b4ce-454dc60b7d02","resolution":{"observed_at":"2026-06-30T16:44:56.433442Z","resolver_source":"arxiv_id","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/2003.12880/citation-record","integrity":"/paper/2003.12880/integrity","json":"/paper/2003.12880/citation-record.json","paper":"/paper/2003.12880"},"outbound":[],"paper":{"arxiv_id":"2003.12880","last_updated":"2020-03-28T19:55:24Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T06:02:51.578196Z","submitted_at":"2020-03-28T19:55:24Z","title":"Federated Residual 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 2 inbound Pith citation observations for arXiv:2003.12880."}