{"as_of":"2026-08-21T22:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9600cd5166229416a83668d6187b0e6da35ea8232b330ce8833679ae97a8dba6","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:23:14.484849Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T22:07:26.583533Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.04904","last_updated":"2023-07-15T17:47:23Z","snapshot_observed_at":"2026-08-21T07:40:20.777356Z","submitted_at":"2023-06-08T03:16:21Z","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.04904","snapshot_observed_at":"2026-08-11T10:23:14.484849Z","title":"Basir, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16738","last_updated":"2024-12-21T19:01:38Z","snapshot_observed_at":"2026-08-13T00:26:49.327599Z","submitted_at":"2024-12-21T19:01:38Z","title":"KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics","version":1},"reference_index":118,"source":"pdf_text","source_observed_at":"2026-08-11T10:23:14.484849Z"},"links":{"cited_paper":"/paper/2306.04904","citing_paper":"/paper/2412.16738"},"observation_digest":"sha256:26e8dbea05f602d01ca8fc3ecb6540854139e8c49ab7b50a01d0248554ad3019","observation_id":"0e7a25fa-3282-4bba-b213-ee82fb1d257b","resolution":{"observed_at":"2026-08-11T10:23:14.484849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04904","last_updated":"2023-07-15T17:47:23Z","snapshot_observed_at":"2026-08-21T07:40:20.777356Z","submitted_at":"2023-06-08T03:16:21Z","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.04904","snapshot_observed_at":"2026-08-06T18:55:23.272395Z","title":"An adaptive augmented lagrangian method for training physics and equality constrained artificial neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.07159","last_updated":"2025-07-09T17:46:59Z","snapshot_observed_at":"2026-08-19T14:53:21.667598Z","submitted_at":"2025-07-09T17:46:59Z","title":"Large-scale portfolio optimization with variational neural annealing","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T18:55:23.272395Z"},"links":{"cited_paper":"/paper/2306.04904","citing_paper":"/paper/2507.07159"},"observation_digest":"sha256:d89974b232dc9c420e89f37aaacbe68cb2ad5cd03855d22bb4f59c0abc0bb2b4","observation_id":"47e8256e-be18-47f1-896e-9ef6dfaf1e8f","resolution":{"observed_at":"2026-08-06T18:55:23.272395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04904","last_updated":"2023-07-15T17:47:23Z","snapshot_observed_at":"2026-08-21T07:40:20.777356Z","submitted_at":"2023-06-08T03:16:21Z","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.04904","snapshot_observed_at":"2026-08-06T14:00:56.953914Z","title":"An adaptive augmented lagrangian method for training physics and equality constrained artificial neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19907","last_updated":"2025-07-26T10:36:24Z","snapshot_observed_at":"2026-08-20T02:17:55.386409Z","submitted_at":"2025-07-26T10:36:24Z","title":"Deep Uzawa for Kinetic Transport with Lagrange-Enforced Boundaries","version":1},"reference_index":1994,"source":"pdf_text","source_observed_at":"2026-08-06T14:00:56.953914Z"},"links":{"cited_paper":"/paper/2306.04904","citing_paper":"/paper/2507.19907"},"observation_digest":"sha256:ea88d21da86effe4ad26bb4cab9ac26c9b1785db60bdb6d49369ed51e346fd13","observation_id":"8defdef5-6953-4f20-9894-06664782e6e5","resolution":{"observed_at":"2026-08-06T14:00:56.953914Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04904","last_updated":"2023-07-15T17:47:23Z","snapshot_observed_at":"2026-08-21T07:40:20.777356Z","submitted_at":"2023-06-08T03:16:21Z","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks","version":2},"cited_work":{"arxiv_id":"2306.04904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.04904","snapshot_observed_at":"2026-07-02T22:07:26.583533Z","title":"An adaptive augmented lagrangian method for training physics and equality constrained artificial neural networks.arXiv preprint arXiv:2306.04904, 2023","venue":null,"work_id":"e95cfd49-85aa-4667-846b-a92aada276e9","year":2023},"citing_paper":{"arxiv_id":"2606.01366","last_updated":"2026-05-31T17:51:01Z","snapshot_observed_at":"2026-08-13T10:24:20.047068Z","submitted_at":"2026-05-31T17:51:01Z","title":"Conservative Discrete Structure Stabilizes Autoregressive Rollouts in a 1D Drift Diffusion Poisson Benchmark","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T15:58:37.202523Z"},"links":{"cited_paper":"/paper/2306.04904","citing_paper":"/paper/2606.01366"},"observation_digest":"sha256:9b73382cef87a8459e74faf2c63d7bf4afcafe658d97931bc53ba9f9b997a611","observation_id":"aaa791a3-4542-4ca5-b2cb-ee3d155ab053","resolution":{"observed_at":"2026-07-01T21:56:16.005185Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04904","last_updated":"2023-07-15T17:47:23Z","snapshot_observed_at":"2026-08-21T07:40:20.777356Z","submitted_at":"2023-06-08T03:16:21Z","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks","version":2},"cited_work":{"arxiv_id":"2306.04904","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.04904","snapshot_observed_at":"2026-07-02T22:07:26.583533Z","title":"An adaptive augmented lagrangian method for training physics and equality constrained artificial neural networks.arXiv preprint arXiv:2306.04904, 2023","venue":null,"work_id":"e95cfd49-85aa-4667-846b-a92aada276e9","year":2023},"citing_paper":{"arxiv_id":"2606.07984","last_updated":"2026-06-06T05:22:03Z","snapshot_observed_at":"2026-08-21T07:40:52.836649Z","submitted_at":"2026-06-06T05:22:03Z","title":"Lagrange multipliers in Maximum likelihood estimations and Least squares problems with Constraints","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T19:09:02.756976Z"},"links":{"cited_paper":"/paper/2306.04904","citing_paper":"/paper/2606.07984"},"observation_digest":"sha256:01d58e74df1613f5f36a56d40db56037af16eb1d95d6a795009f0199955eb8d1","observation_id":"9bda963a-c4a9-4e52-ab5a-fe31b3f06259","resolution":{"observed_at":"2026-07-02T22:07:26.585621Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.04904/citation-record","integrity":"/paper/2306.04904/integrity","json":"/paper/2306.04904/citation-record.json","paper":"/paper/2306.04904"},"outbound":[],"paper":{"arxiv_id":"2306.04904","last_updated":"2023-07-15T17:47:23Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-21T07:40:20.777356Z","submitted_at":"2023-06-08T03:16:21Z","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial 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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.04904."}