{"as_of":"2026-08-13T05:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:abaf14fcdbce22917726f9713ec872dc67de76ab2a2766b031273e7be4a7ea4c","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:23:10.289637Z","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":"2026-08-07T05:27:08.377186Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09689","snapshot_observed_at":"2026-08-12T20:23:10.289637Z","title":"Antisymmetri- crnn: A dynamical system view on recurrent neural networks","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2411.09827","last_updated":"2024-11-14T22:24:59Z","snapshot_observed_at":"2026-08-13T04:40:58.617866Z","submitted_at":"2024-11-14T22:24:59Z","title":"The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T20:23:10.289637Z"},"links":{"cited_paper":"/paper/1902.09689","citing_paper":"/paper/2411.09827"},"observation_digest":"sha256:9567bffb2822d879c9098f67176337c4ba05c7bd1aa842fab302a20a8f59c0a5","observation_id":"7faac380-5a83-43b0-896c-265d1f182091","resolution":{"observed_at":"2026-08-12T20:23:10.289637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09689","snapshot_observed_at":"2026-08-08T15:59:24.342781Z","title":"Chang, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.06311","last_updated":"2025-02-10T10:01:36Z","snapshot_observed_at":"2026-08-10T04:36:17.453810Z","submitted_at":"2025-02-10T10:01:36Z","title":"Analog classical simulation of closed quantum systems","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-08T15:59:24.342781Z"},"links":{"cited_paper":"/paper/1902.09689","citing_paper":"/paper/2502.06311"},"observation_digest":"sha256:1b6cb4f738657b6123c7af48f1932e85c9880f82f9c49cfd5fe5d65fdb44c3a9","observation_id":"6510ce91-6ec5-4d0f-9287-1c8929c19fc5","resolution":{"observed_at":"2026-08-08T15:59:24.342781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09689","snapshot_observed_at":"2026-08-07T12:14:21.509192Z","title":"Antisymmetricrnn: A dynamical system view on recurrent neural networks","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.00588","last_updated":"2025-07-15T15:22:47Z","snapshot_observed_at":"2026-08-10T02:15:59.472388Z","submitted_at":"2025-05-31T14:51:08Z","title":"Temporal Chunking Enhances Recognition of Implicit Sequential Patterns","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:14:21.509192Z"},"links":{"cited_paper":"/paper/1902.09689","citing_paper":"/paper/2506.00588"},"observation_digest":"sha256:412185f0af8e97f692120868fa9ea21fe4ab780df57991f070de3bd93f6cf3e4","observation_id":"e3982352-d0f7-46c0-b14f-7a2fff41db7c","resolution":{"observed_at":"2026-08-07T12:14:21.509192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09689","snapshot_observed_at":"2026-08-07T11:02:56.729571Z","title":"Antisymmetricrnn: A dynamical system view on recurrent neural networks.arXiv preprint arXiv:1902.09689, 2019","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.03889","last_updated":"2025-06-19T07:30:55Z","snapshot_observed_at":"2026-08-10T13:03:29.572692Z","submitted_at":"2025-06-04T12:34:22Z","title":"Temporal horizons in forecasting: a performance-learnability trade-off","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:02:56.729571Z"},"links":{"cited_paper":"/paper/1902.09689","citing_paper":"/paper/2506.03889"},"observation_digest":"sha256:5d4344a25d90fe265eaeb83a58f758e4ddae62426daada9dda63aedf7d328039","observation_id":"982fa725-cb70-4d15-9d92-ca4f5eea2ce7","resolution":{"observed_at":"2026-08-07T11:02:56.729571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":"1902.09689","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.09689","snapshot_observed_at":"2026-08-07T05:27:08.377186Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","venue":"stat.ML","work_id":"ce946380-725f-4aee-be6d-9dbfc68ed6d6","year":2019},"citing_paper":{"arxiv_id":"2506.07975","last_updated":"2025-06-09T17:49:29Z","snapshot_observed_at":"2026-08-09T21:08:05.512838Z","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":65,"source":"pdf_text","source_observed_at":"2026-08-07T05:27:08.173366Z"},"links":{"cited_paper":"/paper/1902.09689","citing_paper":"/paper/2506.07975"},"observation_digest":"sha256:1b3ac0babd7147b9075780c159dc5d71dfc98b9f021b0dc7d8f2f766de5e1ad9","observation_id":"85581ed9-7284-4859-adc2-c471a900c7c8","resolution":{"observed_at":"2026-08-07T05:27:08.380777Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09689","snapshot_observed_at":"2026-08-03T03:21:48.759368Z","title":"AntisymmetricRNN: A dynamical system view on recurrent neural networks.arXiv preprint arXiv:1902.09689, 2019","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2602.08640","last_updated":"2026-07-20T12:14:43Z","snapshot_observed_at":"2026-08-06T10:33:55.722711Z","submitted_at":"2026-02-09T13:35:02Z","title":"Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T03:21:48.759368Z"},"links":{"cited_paper":"/paper/1902.09689","citing_paper":"/paper/2602.08640"},"observation_digest":"sha256:10a0de79363dbbd4f05ae865e53153131ddd662d0122afac2dec8e36d4f0f258","observation_id":"8758c36e-d8a7-48a6-8166-0562399aa850","resolution":{"observed_at":"2026-08-03T03:21:48.759368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1902.09689/citation-record","integrity":"/paper/1902.09689/integrity","json":"/paper/1902.09689/citation-record.json","paper":"/paper/1902.09689"},"outbound":[],"paper":{"arxiv_id":"1902.09689","last_updated":"2019-02-26T01:18:46Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T07:35:24.412482Z","submitted_at":"2019-02-26T01:18:46Z","title":"AntisymmetricRNN: A Dynamical System View on 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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1902.09689."}