{"as_of":"2026-08-18T22:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ca7949037078d17782fd6549b8da320b21e95f4bd2a7ae017e16f70a305a9fa","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":"2026-08-05T12:03:18.199928Z","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":"2026-08-05T12:03:18.312865Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.12282","last_updated":"2024-06-18T05:19:51Z","snapshot_observed_at":"2026-08-17T13:32:15.486580Z","submitted_at":"2024-06-18T05:19:51Z","title":"SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting","version":1},"cited_work":{"arxiv_id":"2406.12282","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.12282","snapshot_observed_at":"2026-08-05T12:03:18.312865Z","title":"SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting","venue":"cs.LG","work_id":"9f97a894-4ab7-45e8-9f1c-34da421489b0","year":2024},"citing_paper":{"arxiv_id":"2509.01997","last_updated":"2025-09-02T06:20:41Z","snapshot_observed_at":"2026-08-11T01:26:45.304612Z","submitted_at":"2025-09-02T06:20:41Z","title":"ACA-Net: Future Graph Learning for Logistical Demand-Supply Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T12:03:18.199928Z"},"links":{"cited_paper":"/paper/2406.12282","citing_paper":"/paper/2509.01997"},"observation_digest":"sha256:9c2f199c4335f98e9d3f62716de3b5ee60a0bbb53710dca7b499b3522dd76ec7","observation_id":"28a4f987-c872-48f6-a559-0536a0c6d3bf","resolution":{"observed_at":"2026-08-05T12:03:18.320629Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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/2406.12282/citation-record","integrity":"/paper/2406.12282/integrity","json":"/paper/2406.12282/citation-record.json","paper":"/paper/2406.12282"},"outbound":[],"paper":{"arxiv_id":"2406.12282","last_updated":"2024-06-18T05:19:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T13:32:15.486580Z","submitted_at":"2024-06-18T05:19:51Z","title":"SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting"},"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:2406.12282."}