{"as_of":"2026-08-09T18:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7a9ec4d401f2c3dfd309c09617424d91a6c78e569e5cbfadcee874a594b02a66","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":"2026-08-09T06:31:02.800959+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":"2026-08-08T12:00:09.843594Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T06:06:26.578430Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.10347","last_updated":"2023-10-11T07:50:09Z","snapshot_observed_at":"2026-08-09T02:46:20.318411Z","submitted_at":"2023-06-17T13:40:15Z","title":"DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.10347","snapshot_observed_at":"2026-08-08T12:00:09.843594Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07858","last_updated":"2025-05-17T14:23:17Z","snapshot_observed_at":"2026-08-09T02:46:06.381863Z","submitted_at":"2025-02-11T16:22:06Z","title":"Mamba Adaptive Anomaly Transformer with association discrepancy for time series","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T12:00:09.843594Z"},"links":{"cited_paper":"/paper/2306.10347","citing_paper":"/paper/2502.07858"},"observation_digest":"sha256:5075f1ceaa94146639bfafef169d8bf46febd1f1f9ae666b7e24c3bc45afcae4","observation_id":"f59b2cab-847e-4d65-9b5a-0a1e8d1952d7","resolution":{"observed_at":"2026-08-08T12:00:09.843594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.10347","last_updated":"2023-10-11T07:50:09Z","snapshot_observed_at":"2026-08-09T02:46:20.318411Z","submitted_at":"2023-06-17T13:40:15Z","title":"DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection","version":2},"cited_work":{"arxiv_id":"2306.10347","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.10347","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dcdetector: Dual attention contrastive representation learning for time series anomaly detection","venue":null,"work_id":"6d5ea231-5cf6-44b4-801b-be0395cb9328","year":2023},"citing_paper":{"arxiv_id":"2605.09685","last_updated":"2026-05-10T18:12:22Z","snapshot_observed_at":"2026-07-06T23:21:44.900680Z","submitted_at":"2026-05-10T18:12:22Z","title":"Learning Unified Representations of Normalcy for Time Series Anomaly Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-12T04:35:37.496154Z"},"links":{"cited_paper":"/paper/2306.10347","citing_paper":"/paper/2605.09685"},"observation_digest":"sha256:9b370e62a66bc8aeacd63e44b4edc7bd49fed2b7d0d352e4c6810c8d36e8b68f","observation_id":"5135bb8a-13c4-4c49-ac92-ea637dcab2b3","resolution":{"observed_at":"2026-05-12T06:06:26.587098Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.10347/citation-record","integrity":"/paper/2306.10347/integrity","json":"/paper/2306.10347/citation-record.json","paper":"/paper/2306.10347"},"outbound":[],"paper":{"arxiv_id":"2306.10347","last_updated":"2023-10-11T07:50:09Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T02:46:20.318411Z","submitted_at":"2023-06-17T13:40:15Z","title":"DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.10347."}