{"as_of":"2026-08-22T23:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a716daac4f005bce2c720a56fda3585f29a615bfa444fab48020d8c0946f3bbb","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":"2026-08-22T06:32:14.747728+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":"2026-08-15T22:48:54.820468Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":11,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.18494","last_updated":"2024-03-27T12:10:30Z","snapshot_observed_at":"2026-08-16T14:05:55.825224Z","submitted_at":"2024-03-27T12:10:30Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18494","snapshot_observed_at":"2026-08-11T10:23:14.210666Z","title":null,"venue":null,"work_id":null,"year":2024},"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":62,"source":"pdf_text","source_observed_at":"2026-08-11T10:23:14.210666Z"},"links":{"cited_paper":"/paper/2403.18494","citing_paper":"/paper/2412.16738"},"observation_digest":"sha256:303c18bfa21899e4e5c6b9c61a767535fb1e4023f2f67ac13d47bb1209cfe397","observation_id":"7a7b1fb2-37ed-4bf2-a02d-2ce5ae4ad2cc","resolution":{"observed_at":"2026-08-11T10:23:14.210666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18494","last_updated":"2024-03-27T12:10:30Z","snapshot_observed_at":"2026-08-16T14:05:55.825224Z","submitted_at":"2024-03-27T12:10:30Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18494","snapshot_observed_at":"2026-08-15T22:48:54.820468Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20300","last_updated":"2025-05-10T03:46:48Z","snapshot_observed_at":"2026-08-18T15:21:59.251214Z","submitted_at":"2025-05-10T03:46:48Z","title":"FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T22:48:54.820468Z"},"links":{"cited_paper":"/paper/2403.18494","citing_paper":"/paper/2505.20300"},"observation_digest":"sha256:c75c7ea030ec2a4b5429206d368501fb8e893870c683657fd3d7f2bf3583c6db","observation_id":"59b0567f-acb6-46dc-8846-4eec532df011","resolution":{"observed_at":"2026-08-15T22:48:54.820468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18494","last_updated":"2024-03-27T12:10:30Z","snapshot_observed_at":"2026-08-16T14:05:55.825224Z","submitted_at":"2024-03-27T12:10:30Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18494","snapshot_observed_at":"2026-08-06T13:58:34.090259Z","title":"arXiv preprint arXiv:2403.18494 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.19888","last_updated":"2025-07-26T09:36:18Z","snapshot_observed_at":"2026-08-09T06:46:06.385009Z","submitted_at":"2025-07-26T09:36:18Z","title":"Multi-Resolution Training-Enhanced Kolmogorov-Arnold Networks for Multi-Scale PDE Problems","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T13:58:34.090259Z"},"links":{"cited_paper":"/paper/2403.18494","citing_paper":"/paper/2507.19888"},"observation_digest":"sha256:18918af7c79ebf2715d680cfc223ec284b1e97eebf6cd417bf5c8cdc5ed9dd5c","observation_id":"55a95b99-d1a3-4d23-8d82-630443939f0f","resolution":{"observed_at":"2026-08-06T13:58:34.090259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18494","last_updated":"2024-03-27T12:10:30Z","snapshot_observed_at":"2026-08-16T14:05:55.825224Z","submitted_at":"2024-03-27T12:10:30Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","version":1},"cited_work":{"arxiv_id":"2403.18494","doi":"10.48550/arxiv.2403.18494","metadata_source":"pith","pith_arxiv_id":"2403.18494","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"J., Toscano, J","venue":"cs.LG","work_id":"a9972b48-e854-4e04-adcc-39db800eb46a","year":2024},"citing_paper":{"arxiv_id":"2604.07075","last_updated":"2026-04-08T13:28:26Z","snapshot_observed_at":"2026-08-14T09:42:02.556092Z","submitted_at":"2026-04-08T13:28:26Z","title":"Estimating bottom topography in shallow water flows","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T18:10:50.642474Z"},"links":{"cited_paper":"/paper/2403.18494","citing_paper":"/paper/2604.07075"},"observation_digest":"sha256:393e7cbc2cfe46111c34a93028cb50dec9d3e77e15c7f4f075179a4c9ede4dac","observation_id":"39b13096-8085-4390-9ae2-1c8e166b5f33","resolution":{"observed_at":"2026-05-10T18:15:42.508506Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18494","last_updated":"2024-03-27T12:10:30Z","snapshot_observed_at":"2026-08-16T14:05:55.825224Z","submitted_at":"2024-03-27T12:10:30Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","version":1},"cited_work":{"arxiv_id":"2403.18494","doi":"10.48550/arxiv.2403.18494","metadata_source":"pith","pith_arxiv_id":"2403.18494","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"J., Toscano, J","venue":"cs.LG","work_id":"a9972b48-e854-4e04-adcc-39db800eb46a","year":2024},"citing_paper":{"arxiv_id":"2607.07020","last_updated":"2026-07-08T05:34:45Z","snapshot_observed_at":"2026-08-15T15:27:42.568108Z","submitted_at":"2026-07-08T05:34:45Z","title":"Learning Turbulence Closures with Physics-Informed Neural Networks for the Rayleigh-Taylor Transition to Turbulence","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-07-09T21:43:53.789848Z"},"links":{"cited_paper":"/paper/2403.18494","citing_paper":"/paper/2607.07020"},"observation_digest":"sha256:5f769634ff65002e1e4992db1f441fb29fb4702a5a5df71e24791835279b6f01","observation_id":"76608982-fc12-461e-b2aa-0a6826dd04b1","resolution":{"observed_at":"2026-07-09T21:46:34.600002Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.18494/citation-record","integrity":"/paper/2403.18494/integrity","json":"/paper/2403.18494/citation-record.json","paper":"/paper/2403.18494"},"outbound":[],"paper":{"arxiv_id":"2403.18494","last_updated":"2024-03-27T12:10:30Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T14:05:55.825224Z","submitted_at":"2024-03-27T12:10:30Z","title":"Learning in PINNs: Phase transition, total diffusion, and generalization"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.18494."}