{"as_of":"2026-08-08T05:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b33b20019696995737341d5ad932f37a9d21f8a6565492785d419b1aaab94376","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:05:52.567281Z","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":"2026-05-12T09:36:26.289960Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.12603","last_updated":"2021-07-27T05:07:48Z","snapshot_observed_at":"2026-08-07T14:28:09.319639Z","submitted_at":"2021-07-27T05:07:48Z","title":"Federated Learning Meets Natural Language Processing: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.12603","snapshot_observed_at":"2026-08-07T12:05:52.567281Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00743","last_updated":"2025-05-31T23:09:26Z","snapshot_observed_at":"2026-08-07T11:56:43.158163Z","submitted_at":"2025-05-31T23:09:26Z","title":"Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:05:52.567281Z"},"links":{"cited_paper":"/paper/2107.12603","citing_paper":"/paper/2506.00743"},"observation_digest":"sha256:f7c274a1f12e88f16a373d7240c700c25fb39be01302f62b2eb3f773bbd425fc","observation_id":"1cf1ff8a-1245-49e5-811d-5663c8593e7c","resolution":{"observed_at":"2026-08-07T12:05:52.567281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.12603","last_updated":"2021-07-27T05:07:48Z","snapshot_observed_at":"2026-08-07T14:28:09.319639Z","submitted_at":"2021-07-27T05:07:48Z","title":"Federated Learning Meets Natural Language Processing: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.12603","snapshot_observed_at":"2026-08-06T21:45:47.296289Z","title":"Feder- ated Learning Meets Natural Language Processing: A Survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00082","last_updated":"2025-06-30T02:56:11Z","snapshot_observed_at":"2026-08-06T21:38:50.137958Z","submitted_at":"2025-06-30T02:56:11Z","title":"Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:45:47.296289Z"},"links":{"cited_paper":"/paper/2107.12603","citing_paper":"/paper/2507.00082"},"observation_digest":"sha256:4446f6ab8f3a9d34ecda7cec529a68ebf6e4aa41b8b490c3b61f0fed6d9bab24","observation_id":"d072e635-a15e-4167-973f-4ed594d51b58","resolution":{"observed_at":"2026-08-06T21:45:47.296289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.12603","last_updated":"2021-07-27T05:07:48Z","snapshot_observed_at":"2026-08-07T14:28:09.319639Z","submitted_at":"2021-07-27T05:07:48Z","title":"Federated Learning Meets Natural Language Processing: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.12603","snapshot_observed_at":"2026-08-06T19:20:25.104909Z","title":"Federated learning meets natural language processing: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.06031","last_updated":"2025-07-08T14:34:32Z","snapshot_observed_at":"2026-08-07T09:30:03.643953Z","submitted_at":"2025-07-08T14:34:32Z","title":"Efficient Federated Learning with Timely Update Dissemination","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:20:25.104909Z"},"links":{"cited_paper":"/paper/2107.12603","citing_paper":"/paper/2507.06031"},"observation_digest":"sha256:23ab761f40653730762b31a85b0359436566ea3fca373148779eb3c3359de46d","observation_id":"65367e42-7881-4543-a76b-033889952a9b","resolution":{"observed_at":"2026-08-06T19:20:25.104909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.12603","last_updated":"2021-07-27T05:07:48Z","snapshot_observed_at":"2026-08-07T14:28:09.319639Z","submitted_at":"2021-07-27T05:07:48Z","title":"Federated Learning Meets Natural Language Processing: A Survey","version":1},"cited_work":{"arxiv_id":"2107.12603","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2107.12603","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2107.12603 (2021)","venue":null,"work_id":"6f07b440-5774-493b-aeab-2cf2be8b000b","year":2021},"citing_paper":{"arxiv_id":"2604.27434","last_updated":"2026-04-30T05:18:44Z","snapshot_observed_at":"2026-07-06T23:12:52.141592Z","submitted_at":"2026-04-30T05:18:44Z","title":"AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-07T10:26:19.049908Z"},"links":{"cited_paper":"/paper/2107.12603","citing_paper":"/paper/2604.27434"},"observation_digest":"sha256:2f0d3435fe506fb6af830bec841241bed889baa6fff477b3070cba162ebe808c","observation_id":"ce0f4b06-cf12-4867-8d94-c3376e66c72b","resolution":{"observed_at":"2026-05-12T09:36:26.291841Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2107.12603/citation-record","integrity":"/paper/2107.12603/integrity","json":"/paper/2107.12603/citation-record.json","paper":"/paper/2107.12603"},"outbound":[],"paper":{"arxiv_id":"2107.12603","last_updated":"2021-07-27T05:07:48Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T14:28:09.319639Z","submitted_at":"2021-07-27T05:07:48Z","title":"Federated Learning Meets Natural Language Processing: A Survey"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2107.12603."}