{"as_of":"2026-08-13T17:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6301b04c77bb51a62dd4323f97d4965987020c322b5da5e7407350119e8db77","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":"2026-08-13T06:32:02.005865+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":"2026-08-10T21:57:15.840968Z","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":"2026-08-10T21:57:16.106112Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.01184","last_updated":"2022-05-02T20:01:44Z","snapshot_observed_at":"2026-08-13T15:51:49.569018Z","submitted_at":"2022-05-02T20:01:44Z","title":"Performance Weighting for Robust Federated Learning Against Corrupted Sources","version":1},"cited_work":{"arxiv_id":"2205.01184","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.01184","snapshot_observed_at":"2026-08-10T21:57:16.106112Z","title":"Performance Weighting for Robust Federated Learning Against Corrupted Sources","venue":"cs.LG","work_id":"38b5a4e2-ceae-407a-ad23-2bad8f9a4eb5","year":2022},"citing_paper":{"arxiv_id":"2501.03223","last_updated":"2025-01-06T18:57:18Z","snapshot_observed_at":"2026-08-12T02:35:48.230870Z","submitted_at":"2025-01-06T18:57:18Z","title":"Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:15.840968Z"},"links":{"cited_paper":"/paper/2205.01184","citing_paper":"/paper/2501.03223"},"observation_digest":"sha256:91cf945f12b686640c6afa2b9c80de6bb51f3942b2c1ab666f01d02202f63459","observation_id":"8403f9e4-ba65-487f-8a40-bc350e14b4d2","resolution":{"observed_at":"2026-08-10T21:57:16.112342Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2205.01184/citation-record","integrity":"/paper/2205.01184/integrity","json":"/paper/2205.01184/citation-record.json","paper":"/paper/2205.01184"},"outbound":[],"paper":{"arxiv_id":"2205.01184","last_updated":"2022-05-02T20:01:44Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T15:51:49.569018Z","submitted_at":"2022-05-02T20:01:44Z","title":"Performance Weighting for Robust Federated Learning Against Corrupted Sources"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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:2205.01184."}