{"as_of":"2026-08-14T20:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8b4bd30124f80a038ac065932acf09169ce990ef693f4666bfa0dea1523f0b61","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":"2026-08-14T06:32:32.682623+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":"2026-08-12T04:40:45.491431Z","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":"2026-06-30T17:14:57.105861Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.14276","last_updated":"2024-01-11T21:01:25Z","snapshot_observed_at":"2026-08-13T04:55:46.704133Z","submitted_at":"2023-12-21T19:57:29Z","title":"Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions","version":3},"cited_work":{"arxiv_id":"2312.14276","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.14276","snapshot_observed_at":"2026-06-30T17:14:57.105861Z","title":"He and J","venue":null,"work_id":"aae17071-9917-4e20-8759-2aaa18351b22","year":2023},"citing_paper":{"arxiv_id":"2404.19756","last_updated":"2025-02-09T21:09:09Z","snapshot_observed_at":"2026-07-06T18:07:47.744531Z","submitted_at":"2024-04-30T17:58:29Z","title":"KAN: Kolmogorov-Arnold Networks","version":5},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-11T23:42:03.773376Z"},"links":{"cited_paper":"/paper/2312.14276","citing_paper":"/paper/2404.19756"},"observation_digest":"sha256:14dcdf4f931ced30e375d1116f7a4f26b7961a4e22fddc052ee726822cb939d5","observation_id":"841895b8-27ee-4c66-b85b-1017b7f5bcfa","resolution":{"observed_at":"2026-05-11T23:42:05.168312Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14276","last_updated":"2024-01-11T21:01:25Z","snapshot_observed_at":"2026-08-13T04:55:46.704133Z","submitted_at":"2023-12-21T19:57:29Z","title":"Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.14276","snapshot_observed_at":"2026-08-12T04:40:45.491431Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01232","last_updated":"2025-03-01T12:20:53Z","snapshot_observed_at":"2026-08-13T06:18:52.053263Z","submitted_at":"2024-12-02T07:53:47Z","title":"Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T04:40:45.491431Z"},"links":{"cited_paper":"/paper/2312.14276","citing_paper":"/paper/2412.01232"},"observation_digest":"sha256:e166ab1ef1ce4968aaf66849b8f26d6015895c8398f8a4a6002a31b694ba6625","observation_id":"c81405ec-f4bb-4928-84e8-e0061d767c00","resolution":{"observed_at":"2026-08-12T04:40:45.491431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14276","last_updated":"2024-01-11T21:01:25Z","snapshot_observed_at":"2026-08-13T04:55:46.704133Z","submitted_at":"2023-12-21T19:57:29Z","title":"Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions","version":3},"cited_work":{"arxiv_id":"2312.14276","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.14276","snapshot_observed_at":"2026-06-30T17:14:57.105861Z","title":"He and J","venue":null,"work_id":"aae17071-9917-4e20-8759-2aaa18351b22","year":2023},"citing_paper":{"arxiv_id":"2605.22557","last_updated":"2026-05-26T09:40:19Z","snapshot_observed_at":"2026-08-14T18:12:31.241242Z","submitted_at":"2026-05-21T14:39:43Z","title":"Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations","version":1},"reference_index":128,"source":"arxiv_source","source_observed_at":"2026-05-22T07:28:49.152516Z"},"links":{"cited_paper":"/paper/2312.14276","citing_paper":"/paper/2605.22557"},"observation_digest":"sha256:0ae3a12719426c0d734f0d82fd8a360e0f278fb66f54736c9ce167325c523c62","observation_id":"baad7947-eed4-4a03-b002-1d2a1029df39","resolution":{"observed_at":"2026-05-22T07:31:14.043023Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14276","last_updated":"2024-01-11T21:01:25Z","snapshot_observed_at":"2026-08-13T04:55:46.704133Z","submitted_at":"2023-12-21T19:57:29Z","title":"Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions","version":3},"cited_work":{"arxiv_id":"2312.14276","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.14276","snapshot_observed_at":"2026-06-30T17:14:57.105861Z","title":"He and J","venue":null,"work_id":"aae17071-9917-4e20-8759-2aaa18351b22","year":2023},"citing_paper":{"arxiv_id":"2605.22557","last_updated":"2026-05-26T09:40:19Z","snapshot_observed_at":"2026-08-14T18:12:31.241242Z","submitted_at":"2026-05-21T14:39:43Z","title":"Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-30T17:08:54.066574Z"},"links":{"cited_paper":"/paper/2312.14276","citing_paper":"/paper/2605.22557"},"observation_digest":"sha256:d57aada8ad28d27ccaee28ec884bf7f4e3dc8016a6e1768c24275580b20ef900","observation_id":"bfabd71f-e33a-4a77-8244-d2f75ca36baa","resolution":{"observed_at":"2026-06-30T17:14:57.107520Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2312.14276/citation-record","integrity":"/paper/2312.14276/integrity","json":"/paper/2312.14276/citation-record.json","paper":"/paper/2312.14276"},"outbound":[],"paper":{"arxiv_id":"2312.14276","last_updated":"2024-01-11T21:01:25Z","latest_version":3,"primary_category":"math.NA","snapshot_observed_at":"2026-08-13T04:55:46.704133Z","submitted_at":"2023-12-21T19:57:29Z","title":"Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2312.14276."}