{"as_of":"2026-08-08T13:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1cc3a3e6ec3ed72e8ad9d51433cb0f91c4ba7c643f35faefc0dad2dad70497e5","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T14:36:20.824321Z","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":"2026-08-06T23:20:59.737521Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.13896","last_updated":"2023-07-26T01:44:02Z","snapshot_observed_at":"2026-07-06T15:58:35.684552Z","submitted_at":"2023-07-26T01:44:02Z","title":"Low-Parameter Federated Learning with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.13896","snapshot_observed_at":"2026-08-07T14:36:20.824321Z","title":"Low-parameter federated learning with large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18494","last_updated":"2025-05-24T04:12:12Z","snapshot_observed_at":"2026-08-07T17:33:55.764479Z","submitted_at":"2025-05-24T04:12:12Z","title":"FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:36:20.824321Z"},"links":{"cited_paper":"/paper/2307.13896","citing_paper":"/paper/2505.18494"},"observation_digest":"sha256:cf857e12cab42d5ef8541735b1f41623b601baed37c56ef14920be58588011f3","observation_id":"d0231e11-4cc1-4c7c-9d09-9a4fd678875c","resolution":{"observed_at":"2026-08-07T14:36:20.824321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.13896","last_updated":"2023-07-26T01:44:02Z","snapshot_observed_at":"2026-07-06T15:58:35.684552Z","submitted_at":"2023-07-26T01:44:02Z","title":"Low-Parameter Federated Learning with Large Language Models","version":1},"cited_work":{"arxiv_id":"2307.13896","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.13896","snapshot_observed_at":"2026-08-06T23:20:59.737521Z","title":"Low-Parameter Federated Learning with Large Language Models","venue":"cs.DC","work_id":"91e691d4-f5dc-43a8-a399-a1d68c48a3ee","year":2023},"citing_paper":{"arxiv_id":"2506.18432","last_updated":"2025-06-24T06:26:34Z","snapshot_observed_at":"2026-08-06T23:13:38.659225Z","submitted_at":"2025-06-23T09:13:54Z","title":"A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:52.726453Z"},"links":{"cited_paper":"/paper/2307.13896","citing_paper":"/paper/2506.18432"},"observation_digest":"sha256:18d07b3bef7e26ac4402adca79a921c8d6e001289e0a98c03cda533c8622e88b","observation_id":"eada7234-5406-4034-aa7c-e0c7ee34e522","resolution":{"observed_at":"2026-08-06T23:20:59.824814Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2307.13896/citation-record","integrity":"/paper/2307.13896/integrity","json":"/paper/2307.13896/citation-record.json","paper":"/paper/2307.13896"},"outbound":[],"paper":{"arxiv_id":"2307.13896","last_updated":"2023-07-26T01:44:02Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-07-06T15:58:35.684552Z","submitted_at":"2023-07-26T01:44:02Z","title":"Low-Parameter Federated Learning with Large Language Models"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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:2307.13896."}