{"as_of":"2026-08-08T02:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a141c2477252d8ab3d994c3fe36952a97472076c33e30c6ad9e4b47a91bcb254","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:14:04.561633Z","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":"2026-08-07T04:46:44.287362Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.08954","last_updated":"2025-05-28T10:55:33Z","snapshot_observed_at":"2026-07-06T18:45:09.719850Z","submitted_at":"2024-07-12T03:18:08Z","title":"PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.08954","snapshot_observed_at":"2026-08-07T13:14:04.561633Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22447","last_updated":"2025-05-28T15:09:56Z","snapshot_observed_at":"2026-08-07T13:04:42.049006Z","submitted_at":"2025-05-28T15:09:56Z","title":"Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:14:04.561633Z"},"links":{"cited_paper":"/paper/2407.08954","citing_paper":"/paper/2505.22447"},"observation_digest":"sha256:4510a61a90eb8a2b4592fcf17789456795d9e4b13314cbcd743136d85c113eba","observation_id":"9234d3cc-c4c1-4ce1-a65e-f6b575f6b90a","resolution":{"observed_at":"2026-08-07T13:14:04.561633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08954","last_updated":"2025-05-28T10:55:33Z","snapshot_observed_at":"2026-07-06T18:45:09.719850Z","submitted_at":"2024-07-12T03:18:08Z","title":"PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning","version":2},"cited_work":{"arxiv_id":"2407.08954","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.08954","snapshot_observed_at":"2026-08-07T04:46:44.287362Z","title":"PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning","venue":"cs.CR","work_id":"83d3c141-c0e8-4494-893c-de3da51ac297","year":2024},"citing_paper":{"arxiv_id":"2506.09870","last_updated":"2025-06-11T15:42:18Z","snapshot_observed_at":"2026-08-07T04:36:01.183081Z","submitted_at":"2025-06-11T15:42:18Z","title":"Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T04:46:42.253814Z"},"links":{"cited_paper":"/paper/2407.08954","citing_paper":"/paper/2506.09870"},"observation_digest":"sha256:2462034ab57132cb48c1cdf5705738dbba524a00d8115122d60c9ddff09d7e18","observation_id":"5708fa45-c1f2-4ee2-a19c-6869b0bc37bb","resolution":{"observed_at":"2026-08-07T04:46:44.331203Z","resolver_source":"local_arxiv","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/2407.08954/citation-record","integrity":"/paper/2407.08954/integrity","json":"/paper/2407.08954/citation-record.json","paper":"/paper/2407.08954"},"outbound":[],"paper":{"arxiv_id":"2407.08954","last_updated":"2025-05-28T10:55:33Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-07-06T18:45:09.719850Z","submitted_at":"2024-07-12T03:18:08Z","title":"PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning"},"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 2 inbound Pith citation observations for arXiv:2407.08954."}