{"as_of":"2026-08-14T09:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:933f65cbf462bf81e4d6b391af1b04d5888f60b65a5ec5a0bc1f80bda7cd275c","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":"2026-08-14T06:32:32.682623+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":"2026-08-12T15:02:32.453897Z","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":"2026-08-12T15:02:32.808532Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.17394","last_updated":"2024-06-01T06:31:26Z","snapshot_observed_at":"2026-08-13T05:39:52.588603Z","submitted_at":"2023-10-26T13:46:18Z","title":"PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2310.17394","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.17394","snapshot_observed_at":"2026-08-12T15:02:32.808532Z","title":"PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks","venue":"cs.LG","work_id":"87456270-800c-4d90-ac54-897cc4e35ae9","year":2023},"citing_paper":{"arxiv_id":"2411.14718","last_updated":"2024-11-22T04:10:49Z","snapshot_observed_at":"2026-08-13T03:55:20.116454Z","submitted_at":"2024-11-22T04:10:49Z","title":"GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T15:02:32.453897Z"},"links":{"cited_paper":"/paper/2310.17394","citing_paper":"/paper/2411.14718"},"observation_digest":"sha256:bb169866b6899b79b922c04b5d60fea9cb9b392ffd9f3e889a01454e04155e55","observation_id":"190748b9-0e80-454b-9a11-4fa9857f9751","resolution":{"observed_at":"2026-08-12T15:02:32.814383Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.17394/citation-record","integrity":"/paper/2310.17394/integrity","json":"/paper/2310.17394/citation-record.json","paper":"/paper/2310.17394"},"outbound":[],"paper":{"arxiv_id":"2310.17394","last_updated":"2024-06-01T06:31:26Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T05:39:52.588603Z","submitted_at":"2023-10-26T13:46:18Z","title":"PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2310.17394."}