{"as_of":"2026-08-07T12:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a9df6ea3f17741c65bc86f082c5a4377807f119e3cbaeda04e284e5841cc3df3","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":"2026-08-06T18:49:16.921394Z","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":"2026-07-04T12:29:51.499326Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1701.05923","last_updated":"2017-01-20T20:53:51Z","snapshot_observed_at":"2026-08-02T13:08:32.140882Z","submitted_at":"2017-01-20T20:53:51Z","title":"Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.05923","snapshot_observed_at":"2026-08-06T18:49:16.921394Z","title":"Dey and F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.07296","last_updated":"2025-07-09T21:43:06Z","snapshot_observed_at":"2026-08-07T09:56:47.034736Z","submitted_at":"2025-07-09T21:43:06Z","title":"Time Series Foundation Models for Multivariate Financial Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T18:49:16.921394Z"},"links":{"cited_paper":"/paper/1701.05923","citing_paper":"/paper/2507.07296"},"observation_digest":"sha256:a210feca935d5cb079f68632e67caa6c4109c29f261ea8c8e39dc2dd90c08c2c","observation_id":"95ab4073-48fc-41df-9198-450d14b1d759","resolution":{"observed_at":"2026-08-06T18:49:16.921394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.05923","last_updated":"2017-01-20T20:53:51Z","snapshot_observed_at":"2026-08-02T13:08:32.140882Z","submitted_at":"2017-01-20T20:53:51Z","title":"Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks","version":1},"cited_work":{"arxiv_id":"1701.05923","doi":null,"metadata_source":"pith","pith_arxiv_id":"1701.05923","snapshot_observed_at":"2026-07-04T12:29:51.499326Z","title":"Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks","venue":"cs.NE","work_id":"3cfb130b-5d14-4bb4-aba1-c89300e98c6d","year":2017},"citing_paper":{"arxiv_id":"2606.23252","last_updated":"2026-06-22T12:34:00Z","snapshot_observed_at":"2026-08-06T11:10:52.234841Z","submitted_at":"2026-06-22T12:34:00Z","title":"Learning to Compute on Dirty Paper","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T06:52:06.932905Z"},"links":{"cited_paper":"/paper/1701.05923","citing_paper":"/paper/2606.23252"},"observation_digest":"sha256:7a619e17ff25462a310892e40d6897d0f1178aeeb62eb0ea96cea63aaaaebf01","observation_id":"fa338a1a-89ff-4d38-a201-92d0e14ac1ca","resolution":{"observed_at":"2026-07-04T12:29:51.500756Z","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/1701.05923/citation-record","integrity":"/paper/1701.05923/integrity","json":"/paper/1701.05923/citation-record.json","paper":"/paper/1701.05923"},"outbound":[],"paper":{"arxiv_id":"1701.05923","last_updated":"2017-01-20T20:53:51Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-02T13:08:32.140882Z","submitted_at":"2017-01-20T20:53:51Z","title":"Gate-Variants of Gated Recurrent Unit (GRU) 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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1701.05923."}