{"as_of":"2026-08-22T14:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:46adcb25cb60733955338c46d315fd7502de2a2b037ebf7a58f04d1ad2fe0c27","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":"2026-08-22T06:32:14.747728+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":"2026-05-20T22:21:03.637418Z","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":"2026-05-20T22:23:47.889751Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.07078","last_updated":"2023-03-24T22:14:19Z","snapshot_observed_at":"2026-08-16T18:45:43.992014Z","submitted_at":"2021-02-14T05:36:25Z","title":"Exploiting Shared Representations for Personalized Federated Learning","version":3},"cited_work":{"arxiv_id":"2102.07078","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.07078","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e097cd52-8652-48fb-847b-309b0840a591","year":2021},"citing_paper":{"arxiv_id":"2605.09855","last_updated":"2026-05-18T00:37:30Z","snapshot_observed_at":"2026-08-14T22:44:32.446622Z","submitted_at":"2026-05-11T01:17:58Z","title":"Concordia: Self-Improving Synthetic Tables for Federated LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-12T04:26:50.410397Z"},"links":{"cited_paper":"/paper/2102.07078","citing_paper":"/paper/2605.09855"},"observation_digest":"sha256:1520c3d9ed6b06c4ec21c5c0951f7caa58fa8e5022f782815d969349b0d27fd1","observation_id":"4633c768-0e08-4b6b-a141-f0c84a12d1b6","resolution":{"observed_at":"2026-05-12T06:16:27.648785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.07078","last_updated":"2023-03-24T22:14:19Z","snapshot_observed_at":"2026-08-16T18:45:43.992014Z","submitted_at":"2021-02-14T05:36:25Z","title":"Exploiting Shared Representations for Personalized Federated Learning","version":3},"cited_work":{"arxiv_id":"2102.07078","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.07078","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e097cd52-8652-48fb-847b-309b0840a591","year":2021},"citing_paper":{"arxiv_id":"2605.09855","last_updated":"2026-05-18T00:37:30Z","snapshot_observed_at":"2026-08-14T22:44:32.446622Z","submitted_at":"2026-05-11T01:17:58Z","title":"Concordia: Self-Improving Synthetic Tables for Federated LLMs","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-20T22:21:03.637418Z"},"links":{"cited_paper":"/paper/2102.07078","citing_paper":"/paper/2605.09855"},"observation_digest":"sha256:c4761cb8533ab5651fddc51d8cb22df3464418c450ee864a8010b5c95b7e08a7","observation_id":"7f36d4df-df2e-4455-be7f-59a48433025d","resolution":{"observed_at":"2026-05-20T22:23:47.892892Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2102.07078/citation-record","integrity":"/paper/2102.07078/integrity","json":"/paper/2102.07078/citation-record.json","paper":"/paper/2102.07078"},"outbound":[],"paper":{"arxiv_id":"2102.07078","last_updated":"2023-03-24T22:14:19Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T18:45:43.992014Z","submitted_at":"2021-02-14T05:36:25Z","title":"Exploiting Shared Representations for Personalized 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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.07078."}