{"as_of":"2026-08-10T08:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e58f470845c80d5e610f17740a116907f4539b97d3de9b372d176da9452311a8","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T21:32:19.367153Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2603.14106/citation-record","integrity":"/paper/2603.14106/integrity","json":"/paper/2603.14106/citation-record.json","paper":"/paper/2603.14106"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Deep networks for system identification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:41d0d908656b5574a287decb7a3242544ec13783a8b4908f3b448d5f8bcdb4a1","observation_id":"7b731ce4-2111-401f-9a4e-275716aa583a","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:f4327803455ec8a990a8b333fa85fd28597854fcf6e1a8c2ce43ed7365a18631","observation_id":"baac7c4c-fae8-454f-9e7f-705eab852e23","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"}},{"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":"2026-08-10T05:01:25.480020Z","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:c810000ba6a5ad96e44a4387a1afd30b1f3b2b4b1ace6b52e6cf7bf2109f6ed7","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"On Recurrent Neural Networks for learning-based control,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:64cde93e8607a6ede7224cfa43572fe48405af03347ae21a88435cff1d106e47","observation_id":"62681e02-9803-40cc-bc85-545c1fb12bf8","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Learning model predictive control with long short-term memory networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:44ef0dcc9c100ab3884e62b3b84c83d1308b44488980812c6ec7a8e273daf5e4","observation_id":"db3a508e-183d-43f4-9830-5c92bfc3769f","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"On the stability properties of Gated Recurrent Units neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:07f6fdbc4325399eda5769fa3cd33e39b2fdf76728fd6fc7717b43dd3ccb4eaf","observation_id":"ef344fc9-a61a-4eee-a405-9b838dda9e20","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"An Incremental Input-to- State Stability Condition for a Class of Recurrent Neural Networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:936bc6f3f4c447b9d437f591ed1b4014a498ebb3893e98027c68df19a232229d","observation_id":"288849bd-08a7-4130-9dd5-305adf704683","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Infinity-norm-based Input-to-State-Stable Long Short- Term Memory networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:15266e5dc94606f116dd25d66affea1d6fc0691e6459f50946b498208105cc8e","observation_id":"ddf28547-2354-4713-9721-44c3a6ff268b","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Robust Offset-Free Constrained Model Predictive Control With Long Short-Term Memory Networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:8bfb48acfa337ac7eb9c9dae9b733e961a3d8c480d98673055ec6a26edbb4bd3","observation_id":"62a6da5b-9d57-4ee9-a3c3-24ac1106ac1a","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:41b5ed081299ec288426e3f52aa14596d662f391d50586afe160cac56ef94fcd","observation_id":"07f5a24e-7c2e-4ecd-babc-4b854634885a","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"}},{"citation":{"cited_paper":{"arxiv_id":"1805.10369","last_updated":"2019-03-02T00:55:07Z","snapshot_observed_at":"2026-07-06T06:41:20.547019Z","submitted_at":"2018-05-25T21:37:35Z","title":"Stable Recurrent Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.10369","snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Stable Recurrent Models,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"cited_paper":"/paper/1805.10369","citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:6d8431e50f292f13c3fa39501fd1ca281cdb7a0acd56c79530c5300415bfd03e","observation_id":"739bbdb4-fda7-4dbb-9396-aa9ce15aeba2","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Deep equilibrium models,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:a974cb4cca9581a7e76b633d238081f1917ea23d5f20422cd235c99ebe8fd121","observation_id":"ab8e13ff-5c53-477b-b591-3cfcedf782ec","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Strogatz,Nonlinear Dynamics and Chaos, ser","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:63219d2ad5485b498a0e3d7561000af288e8c1fd0dc08dd42d3c15339a1a7fda","observation_id":"0276413d-ab11-452e-9cc4-a51ba4cceb6a","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"A compendium of comparison function results,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:81455ea4fe380863f7d988a3e6d23331e8aa0387811b8d68708b65b9b7e8e412","observation_id":"c8bd8db5-671d-4714-a94e-573bbfeb2cc0","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Input-to-state stability for discrete-time nonlinear systems,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:75816dc4a25e6f78af2254f875ee30e040e3e8662d743eb9e4e18898f3c1e90c","observation_id":"fad34332-72cd-45ac-be38-3b281e0e7d37","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Discrete-time Incremental ISS,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:93d18d8a44f1779bc599b3ee650050e076799dc7ea74f3237f16921d72156164","observation_id":"6d703bf3-4b82-4b39-bc4c-f0439bbf25b4","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Goodfellow, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:19127dcbb0f4241c23bd6e7054ee597e5016293b212fcd842d157372e7d4af15","observation_id":"754573eb-9287-4c1d-aa91-0ad9dbdeb747","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":"Were RNNs All We Needed?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:89fa737f990141b0f2ab24b89829cde088ed0b82a0336e81e8696af4791921d9","observation_id":"3d16d89a-b9db-481a-8f76-1091b2c7210d","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T21:32:19.367153Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T21:32:19.367153Z"},"links":{"citing_paper":"/paper/2603.14106"},"observation_digest":"sha256:2265f2837b772ad1ef8e321e479152da736ee095d6ba543155e9d143c852bfe7","observation_id":"92b18025-80e9-4b5a-a2f4-c6e1d74527b1","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"}}],"paper":{"arxiv_id":"2603.14106","last_updated":"2026-06-08T16:29:05Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-08-10T05:01:25.480020Z","submitted_at":"2026-03-14T20:24:23Z","title":"Chaos-Free Networks are Stable Recurrent Neural Networks"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":19},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2603.14106."}