{"as_of":"2026-08-16T18:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9760dead6296ab97d0c327bc7d0243fc233b7744592d930702706067c7960a71","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T21:53:22.547366Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.10844","last_updated":"2022-06-22T05:11:44Z","snapshot_observed_at":"2026-08-16T16:51:17.749232Z","submitted_at":"2022-06-22T05:11:44Z","title":"Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.10844","snapshot_observed_at":"2026-08-15T21:53:22.547366Z","title":"Quantization robust federated learning for efficient inference on heterogeneous devices","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08646","last_updated":"2025-05-13T15:04:55Z","snapshot_observed_at":"2026-08-15T22:33:55.047427Z","submitted_at":"2025-05-13T15:04:55Z","title":"Modular Federated Learning: A Meta-Framework Perspective","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-15T21:53:22.547366Z"},"links":{"cited_paper":"/paper/2206.10844","citing_paper":"/paper/2505.08646"},"observation_digest":"sha256:a7a3c1965cef0a3a3470d3b7af245e151762126752dd4171ab9f5617cf76fd99","observation_id":"94767989-dfe4-4c26-9c5b-e012d9f6f058","resolution":{"observed_at":"2026-08-15T21:53:22.547366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2206.10844/citation-record","integrity":"/paper/2206.10844/integrity","json":"/paper/2206.10844/citation-record.json","paper":"/paper/2206.10844"},"outbound":[],"paper":{"arxiv_id":"2206.10844","last_updated":"2022-06-22T05:11:44Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T16:51:17.749232Z","submitted_at":"2022-06-22T05:11:44Z","title":"Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2206.10844."}