{"as_of":"2026-08-23T00:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dcb457c9cec6a512d60fc2f96cf3686e49053287ed0badf7dd6715cd3708ca33","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":"2026-08-06T11:55:31.562992Z","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":"2026-08-04T21:06:27.817803Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.02776","last_updated":"2023-06-09T07:33:29Z","snapshot_observed_at":"2026-08-16T15:35:40.394345Z","submitted_at":"2023-05-04T12:21:34Z","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02776","snapshot_observed_at":"2026-08-06T11:55:31.562992Z","title":"Efficient personalized federated learning via sparse model-adaptation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22330","last_updated":"2025-07-30T02:24:26Z","snapshot_observed_at":"2026-08-21T21:52:15.726264Z","submitted_at":"2025-07-30T02:24:26Z","title":"Hypernetworks for Model-Heterogeneous Personalized Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T11:55:31.562992Z"},"links":{"cited_paper":"/paper/2305.02776","citing_paper":"/paper/2507.22330"},"observation_digest":"sha256:4fb4cf6ce56646b8db13c1d69a1c8b077f32aa9701f35f8d6056ccb72c1ef6bf","observation_id":"94b6d5b1-5724-4555-98f2-03e95d2ad324","resolution":{"observed_at":"2026-08-06T11:55:31.562992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02776","last_updated":"2023-06-09T07:33:29Z","snapshot_observed_at":"2026-08-16T15:35:40.394345Z","submitted_at":"2023-05-04T12:21:34Z","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","version":2},"cited_work":{"arxiv_id":"2305.02776","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.02776","snapshot_observed_at":"2026-08-04T21:06:27.817803Z","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","venue":"cs.LG","work_id":"dd48731d-666c-4521-81a9-6726a7c01a7d","year":2023},"citing_paper":{"arxiv_id":"2509.08233","last_updated":"2025-09-10T02:19:56Z","snapshot_observed_at":"2026-08-14T20:31:36.327522Z","submitted_at":"2025-09-10T02:19:56Z","title":"Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-04T21:06:26.001916Z"},"links":{"cited_paper":"/paper/2305.02776","citing_paper":"/paper/2509.08233"},"observation_digest":"sha256:f8eb5c7f7af883202bd96201e706093addc559186b87ac4abd98ee282f537951","observation_id":"6b709a45-698a-4302-866f-f5f34e0f8f01","resolution":{"observed_at":"2026-08-04T21:06:27.821261Z","resolver_source":"local_arxiv","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/2305.02776/citation-record","integrity":"/paper/2305.02776/integrity","json":"/paper/2305.02776/citation-record.json","paper":"/paper/2305.02776"},"outbound":[],"paper":{"arxiv_id":"2305.02776","last_updated":"2023-06-09T07:33:29Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T15:35:40.394345Z","submitted_at":"2023-05-04T12:21:34Z","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation"},"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 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.02776."}