{"as_of":"2026-08-11T18:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ddf000caa7b3562ee74244773a365b369416beebe836c273988fc257ebc6adb2","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T16:11:46.032703Z","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":"2026-08-06T23:53:12.254478Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1711.00811","last_updated":"2018-02-07T21:43:25Z","snapshot_observed_at":"2026-08-09T02:20:50.224151Z","submitted_at":"2017-11-02T16:49:19Z","title":"Expressive power of recurrent neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.00811","snapshot_observed_at":"2026-08-08T16:11:46.032703Z","title":"Expressive power of recurrent neural networks, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.06300","last_updated":"2025-02-10T09:43:43Z","snapshot_observed_at":"2026-08-10T00:28:52.614340Z","submitted_at":"2025-02-10T09:43:43Z","title":"The impact of allocation strategies in subset learning on the expressive power of neural networks","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T16:11:46.032703Z"},"links":{"cited_paper":"/paper/1711.00811","citing_paper":"/paper/2502.06300"},"observation_digest":"sha256:cc974ceeac92f6c0da4e1918be89b4f616cc8b3fbd9c777b857ea7603c8e7c1f","observation_id":"239fb816-a744-4ed0-9333-ee38cecba0b5","resolution":{"observed_at":"2026-08-08T16:11:46.032703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00811","last_updated":"2018-02-07T21:43:25Z","snapshot_observed_at":"2026-08-09T02:20:50.224151Z","submitted_at":"2017-11-02T16:49:19Z","title":"Expressive power of recurrent neural networks","version":2},"cited_work":{"arxiv_id":"1711.00811","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.00811","snapshot_observed_at":"2026-08-06T23:53:12.254478Z","title":"Expressive power of recurrent neural networks","venue":"cs.LG","work_id":"ca1c5bd2-226a-469b-961e-674e74c1ce79","year":2017},"citing_paper":{"arxiv_id":"2506.16032","last_updated":"2025-06-19T05:07:07Z","snapshot_observed_at":"2026-08-07T22:43:08.027896Z","submitted_at":"2025-06-19T05:07:07Z","title":"A Scalable Factorization Approach for High-Order Structured Tensor Recovery","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:10.412443Z"},"links":{"cited_paper":"/paper/1711.00811","citing_paper":"/paper/2506.16032"},"observation_digest":"sha256:e4faf85513756fbe7493cbde0080b85bd20d865fea80f0fbf9ce133ddff106e1","observation_id":"ea5732e2-a6fc-4895-b46f-d0a6148a24bd","resolution":{"observed_at":"2026-08-06T23:53:12.258048Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00811","last_updated":"2018-02-07T21:43:25Z","snapshot_observed_at":"2026-08-09T02:20:50.224151Z","submitted_at":"2017-11-02T16:49:19Z","title":"Expressive power of recurrent neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.00811","snapshot_observed_at":"2026-08-02T05:49:17.903439Z","title":"arXiv preprint arXiv:1711.00811 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13246","last_updated":"2026-08-01T19:18:42Z","snapshot_observed_at":"2026-08-07T16:33:22.195520Z","submitted_at":"2026-07-14T20:21:38Z","title":"Reassessing Muon for Matrix Factorization","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-02T05:49:17.903439Z"},"links":{"cited_paper":"/paper/1711.00811","citing_paper":"/paper/2607.13246"},"observation_digest":"sha256:8db65f613f526fc6b12b6c28b14cb526e1bf9eb4f0af382bf17e0b6399ec54b9","observation_id":"df90365b-ea43-4222-986f-4b7a0ba06ac8","resolution":{"observed_at":"2026-08-02T05:49:17.903439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00811","last_updated":"2018-02-07T21:43:25Z","snapshot_observed_at":"2026-08-09T02:20:50.224151Z","submitted_at":"2017-11-02T16:49:19Z","title":"Expressive power of recurrent neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.00811","snapshot_observed_at":"2026-08-04T04:22:54.780874Z","title":"arXiv preprint arXiv:1711.00811 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13246","last_updated":"2026-08-01T19:18:42Z","snapshot_observed_at":"2026-08-07T16:33:22.195520Z","submitted_at":"2026-07-14T20:21:38Z","title":"Reassessing Muon for Matrix Factorization","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T04:22:54.780874Z"},"links":{"cited_paper":"/paper/1711.00811","citing_paper":"/paper/2607.13246"},"observation_digest":"sha256:b49156b1f6ae6d6cc7c09b05126c22add0054bd5dfe0b8460b8a4007dbd6ade0","observation_id":"95192450-50da-42f0-859e-d1a086ab72c3","resolution":{"observed_at":"2026-08-04T04:22:54.780874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1711.00811/citation-record","integrity":"/paper/1711.00811/integrity","json":"/paper/1711.00811/citation-record.json","paper":"/paper/1711.00811"},"outbound":[],"paper":{"arxiv_id":"1711.00811","last_updated":"2018-02-07T21:43:25Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T02:20:50.224151Z","submitted_at":"2017-11-02T16:49:19Z","title":"Expressive power of recurrent 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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1711.00811."}