{"as_of":"2026-08-12T19:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3163cbb3e7081e63ad03185f4079de2c40f0f9012d8983b956ce6c2e7c8aca1a","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":"2026-08-12T06:34:41.77262+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":"2026-08-11T12:42:04.598666Z","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":"2026-05-13T17:58:04.191350Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1810.11509","last_updated":"2020-08-16T07:03:47Z","snapshot_observed_at":"2026-07-06T07:10:53.084925Z","submitted_at":"2018-10-26T19:13:33Z","title":"Neural Network-Based Approach to Phase Space Integration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.11509","snapshot_observed_at":"2026-08-11T12:42:04.598666Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13982","last_updated":"2024-12-18T16:03:37Z","snapshot_observed_at":"2026-08-12T06:31:50.564545Z","submitted_at":"2024-12-18T16:03:37Z","title":"LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:42:04.598666Z"},"links":{"cited_paper":"/paper/1810.11509","citing_paper":"/paper/2412.13982"},"observation_digest":"sha256:93c9aeaefd6f8fb7e4cdbd6f06b14a0eb814949ef186488359750f4a15ab15be","observation_id":"a81b4c55-3cc6-4486-8031-756808a683b2","resolution":{"observed_at":"2026-08-11T12:42:04.598666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.11509","last_updated":"2020-08-16T07:03:47Z","snapshot_observed_at":"2026-07-06T07:10:53.084925Z","submitted_at":"2018-10-26T19:13:33Z","title":"Neural Network-Based Approach to Phase Space Integration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.11509","snapshot_observed_at":"2026-08-04T22:58:02.131586Z","title":"[33]I.-K","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.07068","last_updated":"2026-07-23T14:00:29Z","snapshot_observed_at":"2026-08-10T13:47:17.435538Z","submitted_at":"2025-09-08T18:00:01Z","title":"FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-04T22:58:02.131586Z"},"links":{"cited_paper":"/paper/1810.11509","citing_paper":"/paper/2509.07068"},"observation_digest":"sha256:b99fa89e4ae50e1ced43a93c365597d18f4b83c4c3dbcb447e58584c7fee4c64","observation_id":"8a8bb96d-c4c9-4050-a561-78cce41b34c7","resolution":{"observed_at":"2026-08-04T22:58:02.131586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.11509","last_updated":"2020-08-16T07:03:47Z","snapshot_observed_at":"2026-07-06T07:10:53.084925Z","submitted_at":"2018-10-26T19:13:33Z","title":"Neural Network-Based Approach to Phase Space Integration","version":3},"cited_work":{"arxiv_id":"1810.11509","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1810.11509","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"cbaf0fd9-e566-4304-b45a-7ad50e8c98bf","year":2020},"citing_paper":{"arxiv_id":"2604.03511","last_updated":"2026-04-03T23:18:12Z","snapshot_observed_at":"2026-08-12T12:33:37.063338Z","submitted_at":"2026-04-03T23:18:12Z","title":"Monte Carlo Event Generation with Continuous Normalizing Flows","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-13T17:54:22.245072Z"},"links":{"cited_paper":"/paper/1810.11509","citing_paper":"/paper/2604.03511"},"observation_digest":"sha256:1d9697cdc245012ab01e0dd7db96576357ec234afca6727cf1524e76d217c293","observation_id":"6e73bdf3-4dfe-4772-98c6-428477e4e786","resolution":{"observed_at":"2026-05-13T17:58:04.192586Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.11509","last_updated":"2020-08-16T07:03:47Z","snapshot_observed_at":"2026-07-06T07:10:53.084925Z","submitted_at":"2018-10-26T19:13:33Z","title":"Neural Network-Based Approach to Phase Space Integration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.11509","snapshot_observed_at":"2026-08-05T00:48:46.584740Z","title":"SciPost Phys.9, 053 (2020) https://doi.org/10.21468/SciPostPhys.9.4","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.00529","last_updated":"2026-08-01T08:41:30Z","snapshot_observed_at":"2026-08-12T15:29:05.774634Z","submitted_at":"2026-08-01T08:41:30Z","title":"Schr\\\"{o}dinger Generator for High-Dimensional Integration and Sampling on Quantum Many-Body States","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T00:48:46.584740Z"},"links":{"cited_paper":"/paper/1810.11509","citing_paper":"/paper/2608.00529"},"observation_digest":"sha256:df25a9615f1721cc2c2112e6e3a5d32c97efd48a80ee57a2868f87eb6002c34d","observation_id":"e46fef4f-eeaf-49d4-b3ad-c5c56c251ed4","resolution":{"observed_at":"2026-08-05T00:48:46.584740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1810.11509/citation-record","integrity":"/paper/1810.11509/integrity","json":"/paper/1810.11509/citation-record.json","paper":"/paper/1810.11509"},"outbound":[],"paper":{"arxiv_id":"1810.11509","last_updated":"2020-08-16T07:03:47Z","latest_version":3,"primary_category":"hep-ph","snapshot_observed_at":"2026-07-06T07:10:53.084925Z","submitted_at":"2018-10-26T19:13:33Z","title":"Neural Network-Based Approach to Phase Space Integration"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1810.11509."}