{"as_of":"2026-08-13T06:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f4eccb3910258a0184cc7f597393194399ba142d2bddfbcee21722dabd745521","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":"2026-08-12T06:34:41.77262+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":"2026-08-11T04:45:42.312626Z","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":"2026-08-11T04:45:42.632263Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.08643","last_updated":"2023-10-27T04:14:43Z","snapshot_observed_at":"2026-08-03T20:00:36.264726Z","submitted_at":"2023-08-16T19:36:01Z","title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly","version":3},"cited_work":{"arxiv_id":"2308.08643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.08643","snapshot_observed_at":"2026-08-11T04:45:42.632263Z","title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly","venue":"cs.LG","work_id":"5b5b9443-e813-4654-8880-5434b973023e","year":2023},"citing_paper":{"arxiv_id":"2412.18460","last_updated":"2025-05-17T02:40:42Z","snapshot_observed_at":"2026-08-12T22:03:14.324247Z","submitted_at":"2024-12-24T14:39:47Z","title":"GeFL: Model-Agnostic Federated Learning with Generative Models","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T04:45:42.312626Z"},"links":{"cited_paper":"/paper/2308.08643","citing_paper":"/paper/2412.18460"},"observation_digest":"sha256:f50c19039cfe0011f22b6df7fb1098f7561d28362e510a22b7abfe59a085a7aa","observation_id":"acd711e3-95cd-469e-b5df-c516a14b8080","resolution":{"observed_at":"2026-08-11T04:45:42.637969Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2308.08643/citation-record","integrity":"/paper/2308.08643/integrity","json":"/paper/2308.08643/citation-record.json","paper":"/paper/2308.08643"},"outbound":[],"paper":{"arxiv_id":"2308.08643","last_updated":"2023-10-27T04:14:43Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T20:00:36.264726Z","submitted_at":"2023-08-16T19:36:01Z","title":"Towards Personalized Federated Learning via Heterogeneous Model Reassembly"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2308.08643."}