{"as_of":"2026-08-07T22:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3954b7ef6b161ba84843c445d3567565deb863da957b05cdfad96a7ea316edce","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:27:08.208585Z","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":"2026-08-06T17:51:45.024848Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1612.06212","last_updated":"2016-12-19T14:59:14Z","snapshot_observed_at":"2026-07-06T05:23:19.120993Z","submitted_at":"2016-12-19T14:59:14Z","title":"A recurrent neural network without chaos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.06212","snapshot_observed_at":"2026-08-07T05:27:08.208585Z","title":"arXiv preprint arXiv:1612.06212 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.07975","last_updated":"2025-06-09T17:49:29Z","snapshot_observed_at":"2026-08-07T18:19:50.414250Z","submitted_at":"2025-06-09T17:49:29Z","title":"Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T05:27:08.208585Z"},"links":{"cited_paper":"/paper/1612.06212","citing_paper":"/paper/2506.07975"},"observation_digest":"sha256:1cb7222f0ae4a1271b88fd403a4d50c90cd282780533d6d42f5150d6ced2a2bc","observation_id":"fba2e746-70a6-4557-81fb-d34e43992eee","resolution":{"observed_at":"2026-08-07T05:27:08.208585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.06212","last_updated":"2016-12-19T14:59:14Z","snapshot_observed_at":"2026-07-06T05:23:19.120993Z","submitted_at":"2016-12-19T14:59:14Z","title":"A recurrent neural network without chaos","version":1},"cited_work":{"arxiv_id":"1612.06212","doi":null,"metadata_source":"pith","pith_arxiv_id":"1612.06212","snapshot_observed_at":"2026-08-06T17:51:45.024848Z","title":"A recurrent neural network without chaos","venue":"cs.NE","work_id":"8a820dad-1813-4488-a18e-a35f5769cfce","year":2016},"citing_paper":{"arxiv_id":"2507.09897","last_updated":"2025-08-06T13:21:56Z","snapshot_observed_at":"2026-08-06T17:42:12.365852Z","submitted_at":"2025-07-14T04:07:43Z","title":"Algorithm Development in Neural Networks: Insights from the Streaming Parity Task","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:51:44.731232Z"},"links":{"cited_paper":"/paper/1612.06212","citing_paper":"/paper/2507.09897"},"observation_digest":"sha256:bc0bf7452535258bf3b0cdc1f21a0f87008b1250b87d47b2225d1e47ca4fcadf","observation_id":"60cc5dd8-d8fa-4a62-85cb-2a9832f545f0","resolution":{"observed_at":"2026-08-06T17:51:45.028992Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"1612.06212","last_updated":"2016-12-19T14:59:14Z","snapshot_observed_at":"2026-07-06T05:23:19.120993Z","submitted_at":"2016-12-19T14:59:14Z","title":"A recurrent neural network without chaos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.06212","snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"A recurrent neural network without chaos,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-07-14T21:32:18.867288Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"cited_paper":"/paper/1612.06212","citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:3027be1bd32e43eecce299570bb9069e714e759e2f97b0b353fe2f7b1846a7e0","observation_id":"190f1e9e-c994-4fdb-8a01-295af1930f71","resolution":{"observed_at":"2026-07-14T21:32:19.367153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1612.06212/citation-record","integrity":"/paper/1612.06212/integrity","json":"/paper/1612.06212/citation-record.json","paper":"/paper/1612.06212"},"outbound":[],"paper":{"arxiv_id":"1612.06212","last_updated":"2016-12-19T14:59:14Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-07-06T05:23:19.120993Z","submitted_at":"2016-12-19T14:59:14Z","title":"A recurrent neural network without chaos"},"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 3 inbound Pith citation observations for arXiv:1612.06212."}