{"as_of":"2026-08-18T07:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5e6493718b7ab6e480495b77610ee93d0b69a8b78c0ed35bfb27f40d4293e306","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":"2026-08-18T06:34:40.430872+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":"2026-08-15T21:24:27.334512Z","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":"2026-08-15T21:24:27.611867Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.06473","last_updated":"2021-03-11T05:39:52Z","snapshot_observed_at":"2026-08-16T18:39:43.417910Z","submitted_at":"2021-03-11T05:39:52Z","title":"Multi-Task Federated Reinforcement Learning with Adversaries","version":1},"cited_work":{"arxiv_id":"2103.06473","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.06473","snapshot_observed_at":"2026-08-15T21:24:27.611867Z","title":"Multi-Task Federated Reinforcement Learning with Adversaries","venue":"cs.LG","work_id":"453b5902-3a3d-41eb-95e2-bab73841b8bf","year":2021},"citing_paper":{"arxiv_id":"2505.09959","last_updated":"2025-05-15T04:41:21Z","snapshot_observed_at":"2026-08-16T10:58:23.415890Z","submitted_at":"2025-05-15T04:41:21Z","title":"Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:27.334512Z"},"links":{"cited_paper":"/paper/2103.06473","citing_paper":"/paper/2505.09959"},"observation_digest":"sha256:b0ca8d0142f5f1c840f2ec5358d67ba475ab2ca85b0dc9c33c9a24655015e4a9","observation_id":"c9604ca5-4444-4e33-a118-5baa0849c3f3","resolution":{"observed_at":"2026-08-15T21:24:27.615399Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2103.06473/citation-record","integrity":"/paper/2103.06473/integrity","json":"/paper/2103.06473/citation-record.json","paper":"/paper/2103.06473"},"outbound":[],"paper":{"arxiv_id":"2103.06473","last_updated":"2021-03-11T05:39:52Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T18:39:43.417910Z","submitted_at":"2021-03-11T05:39:52Z","title":"Multi-Task Federated Reinforcement Learning with Adversaries"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2103.06473."}