{"as_of":"2026-08-07T15:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:381c765c520800f5cd5a53af8e3430227159afff8daac8c6cee686ca5886a711","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:49:00.476226Z","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":"2026-07-08T08:54:48.836832Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.00573","last_updated":"2025-06-13T11:46:16Z","snapshot_observed_at":"2026-07-06T18:55:37.298696Z","submitted_at":"2024-08-01T14:06:34Z","title":"Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00573","snapshot_observed_at":"2026-08-05T14:49:00.476226Z","title":"Convergence analysis of natural gradient descent for over-parameterized physics-informed neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21022","last_updated":"2026-06-08T19:22:55Z","snapshot_observed_at":"2026-08-05T14:48:57.900801Z","submitted_at":"2025-08-28T17:24:59Z","title":"A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-05T14:49:00.476226Z"},"links":{"cited_paper":"/paper/2408.00573","citing_paper":"/paper/2508.21022"},"observation_digest":"sha256:c08864d301890f357d9b382134700d5700db064fd07dcdc6a907e1e39d3fc326","observation_id":"2cb46781-6a41-4457-9d95-d86ccdc58040","resolution":{"observed_at":"2026-08-05T14:49:00.476226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00573","last_updated":"2025-06-13T11:46:16Z","snapshot_observed_at":"2026-07-06T18:55:37.298696Z","submitted_at":"2024-08-01T14:06:34Z","title":"Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks","version":4},"cited_work":{"arxiv_id":"2408.00573","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.00573","snapshot_observed_at":"2026-07-08T08:54:48.836832Z","title":"Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks","venue":"cs.LG","work_id":"b7dbe12e-3fcd-405e-9394-317cf0c463ec","year":2024},"citing_paper":{"arxiv_id":"2607.06369","last_updated":"2026-07-21T13:50:03Z","snapshot_observed_at":"2026-07-24T23:20:51.902898Z","submitted_at":"2026-07-07T15:06:30Z","title":"Feature Learning for the High Dimensional Stationary Sch\\\"odinger Equation with Deep Ritz Method","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-08T08:53:58.014772Z"},"links":{"cited_paper":"/paper/2408.00573","citing_paper":"/paper/2607.06369"},"observation_digest":"sha256:0e09bfe30480064ce1af7de771bf416f52bf001d67a457ef551407ac95555c8f","observation_id":"a55e99b0-a210-4399-86ae-1e65c124e37d","resolution":{"observed_at":"2026-07-08T08:54:48.838570Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00573","last_updated":"2025-06-13T11:46:16Z","snapshot_observed_at":"2026-07-06T18:55:37.298696Z","submitted_at":"2024-08-01T14:06:34Z","title":"Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00573","snapshot_observed_at":"2026-07-14T13:34:45.196896Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10200","last_updated":"2026-07-11T08:25:26Z","snapshot_observed_at":"2026-07-16T23:18:14.574885Z","submitted_at":"2026-07-11T08:25:26Z","title":"The Differential Neural Tangent Kernel and Its Positivity","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T13:34:45.196896Z"},"links":{"cited_paper":"/paper/2408.00573","citing_paper":"/paper/2607.10200"},"observation_digest":"sha256:0a0ee8558f3b9f2a474aeab242155ecdeba417af89b8cb79fde0011acfa3b3cb","observation_id":"a402d550-22df-4de6-8893-982de62af603","resolution":{"observed_at":"2026-07-14T13:34:45.196896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.00573/citation-record","integrity":"/paper/2408.00573/integrity","json":"/paper/2408.00573/citation-record.json","paper":"/paper/2408.00573"},"outbound":[],"paper":{"arxiv_id":"2408.00573","last_updated":"2025-06-13T11:46:16Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:55:37.298696Z","submitted_at":"2024-08-01T14:06:34Z","title":"Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed 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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.00573."}