{"as_of":"2026-08-12T18:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67d553cd85e12fdbdfdbd498132f861b9c300313b01b16fd9b1a156c73b89df2","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:24:32.557619Z","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":"2026-05-11T09:56:01.599284Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.07761","last_updated":"2024-09-10T16:03:23Z","snapshot_observed_at":"2026-07-06T16:06:26.409406Z","submitted_at":"2023-08-15T13:29:14Z","title":"NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07761","snapshot_observed_at":"2026-08-11T20:24:32.557619Z","title":"Nefl: Nested federated learning for heterogeneous clients","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05823","last_updated":"2024-12-08T05:50:04Z","snapshot_observed_at":"2026-08-11T20:16:39.659602Z","submitted_at":"2024-12-08T05:50:04Z","title":"DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T20:24:32.557619Z"},"links":{"cited_paper":"/paper/2308.07761","citing_paper":"/paper/2412.05823"},"observation_digest":"sha256:f2fd3aee3f3d295d96988fce56901fd4aaac1ab9cdac452ddf670bcf4b2285db","observation_id":"1f81c8f2-4b18-4dfe-b08c-6fbff691885c","resolution":{"observed_at":"2026-08-11T20:24:32.557619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07761","last_updated":"2024-09-10T16:03:23Z","snapshot_observed_at":"2026-07-06T16:06:26.409406Z","submitted_at":"2023-08-15T13:29:14Z","title":"NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07761","snapshot_observed_at":"2026-08-11T04:45:42.222834Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.18460","last_updated":"2025-05-17T02:40:42Z","snapshot_observed_at":"2026-08-12T10:47:06.091162Z","submitted_at":"2024-12-24T14:39:47Z","title":"GeFL: Model-Agnostic Federated Learning with Generative Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T04:45:42.222834Z"},"links":{"cited_paper":"/paper/2308.07761","citing_paper":"/paper/2412.18460"},"observation_digest":"sha256:5efb9e4a0dd00bc8cafb48b283a3f019bbdef8d4431f00e8c31335983a318886","observation_id":"8e4c8eb8-4eef-4f29-a0b6-a8653db0f667","resolution":{"observed_at":"2026-08-11T04:45:42.222834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07761","last_updated":"2024-09-10T16:03:23Z","snapshot_observed_at":"2026-07-06T16:06:26.409406Z","submitted_at":"2023-08-15T13:29:14Z","title":"NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients","version":3},"cited_work":{"arxiv_id":"2308.07761","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.07761","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nefl: Nested model scaling for federated learning with system heterogeneous clients.arXiv preprint arXiv:2308.07761","venue":null,"work_id":"73169baf-f2c3-41ad-9059-a857dbf1db5a","year":null},"citing_paper":{"arxiv_id":"2604.11278","last_updated":"2026-04-13T10:38:21Z","snapshot_observed_at":"2026-08-11T14:05:11.005462Z","submitted_at":"2026-04-13T10:38:21Z","title":"Representation-Aligned Multi-Scale Personalization for Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T15:45:17.555896Z"},"links":{"cited_paper":"/paper/2308.07761","citing_paper":"/paper/2604.11278"},"observation_digest":"sha256:3238177a142cc97178fc6f8c5cc5701eddf6c2268b398e9dd9aa2649bf77eee4","observation_id":"93bb77e6-e434-4e0a-8ed4-3fd149c0cde4","resolution":{"observed_at":"2026-05-11T09:56:01.608344Z","resolver_source":"arxiv_id","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/2308.07761/citation-record","integrity":"/paper/2308.07761/integrity","json":"/paper/2308.07761/citation-record.json","paper":"/paper/2308.07761"},"outbound":[],"paper":{"arxiv_id":"2308.07761","last_updated":"2024-09-10T16:03:23Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T16:06:26.409406Z","submitted_at":"2023-08-15T13:29:14Z","title":"NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients"},"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 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2308.07761."}